Hypothesis Pruning – Meta-Guide.com

Notes:

Hypothesis pruning is a technique used to reduce the number of hypotheses (possible explanations or predictions) that are being considered by a machine learning algorithm. It is often used in decision tree learning and other types of machine learning algorithms that generate a large number of hypotheses and need to select the best one based on the available data.

The goal of hypothesis pruning is to improve the performance and efficiency of the machine learning algorithm by reducing the number of hypotheses that need to be considered. This can be especially important when the data set is large or complex, as the algorithm may generate a very large number of hypotheses that are difficult to evaluate.

There are several different approaches to hypothesis pruning, including:

Pre-pruning: Pre-pruning involves setting limits on the size or complexity of the decision tree or other machine learning model. This can help to prevent the algorithm from generating too many hypotheses and becoming over-fitted to the data.
Post-pruning: Post-pruning involves trimming the decision tree or other machine learning model after it has been generated, by removing branches or nodes that do not contribute significantly to the accuracy of the model.
Early stopping: Early stopping involves interrupting the learning process before the model has fully converged, based on the performance of the model on a validation set. This can help to prevent the model from over-fitting the data and generate a more generalizable model.

Hypothesis pruning and spellneme units are both techniques that are used to improve the performance and efficiency of machine learning algorithms.

A statistical bi-directional letter-to-sound (L2S) model is a type of machine learning model that is used to automatically learn the spelling and pronunciation of words. It is based on the idea that the spelling and pronunciation of a word are related, and that it is possible to predict the pronunciation of a word from its spelling and vice versa.

The core modules of a statistical L2S model are known as spelling units or spellneme units. These units represent the smallest unit of spelling that can be pronounced in a consistent way. For example, the spelling unit “ight” might represent the sound /a?t/ in the word “right,” and the spelling unit “ough” might represent the sound /o?/ in the word “though.”

Spellneme units are used to represent the relationships between spelling and pronunciation, and to allow the L2S model to learn the correct pronunciation of new words based on their spelling. They are a key component of statistical L2S models, and play a significant role in their ability to accurately predict the pronunciation of words.

Wikipedia:

Speedpath analysis based on Hypothesis Pruning and ranking N Callegari, LC Wang… – … Conference, 2009. DAC’09 …, 2009 – ieeexplore.ieee.org Abstract In optimizing high-performance designs, speed limiting paths (speedpaths) impact the performance and power trade-off. Timing tools attempt to model and capture all such paths on a chip. Due to the high performance nature of these designs, critical paths … Cited by 11 – Related articles – All 2 versions

Hypothesis Pruning and ranking for large plan recognition problems [PDF] from usukitacs.com G Sukthankar… – Proc. of AAAI, 2008 – aaai.org Abstract This paper addresses the problem of plan recognition for multi-agent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. Teams split into subteams to work in parallel, merge with other teams … Cited by 10 – Related articles – All 16 versions

A scientific workflow construction command line [PDF] from isi.edu PT Groth… – Proceedings of the 14th international conference on …, 2009 – dl.acm.org … ordering). As it interacts with the user, W-CMD cycles through the following phases: Suggestion Generation, Command Line Parsing, Hypothesis Generation, and Hypothesis Pruning. The cycle can be summarized as follows. … Cited by 5 – Related articles – All 9 versions

Cascade object detection with deformable part models [PDF] from videolectures.net PF Felzenszwalb, RB Girshick… – Computer vision and …, 2010 – ieeexplore.ieee.org … We focus primarily on the case of star-structured models and show how a simple algorithm based on par- tial Hypothesis Pruning can speed up object detection by more than one order of magnitude without sacrificing de- tection accuracy. … Cited by 64 – Related articles – All 21 versions

[PDF] Improved JPDA Algorithm with Measurements Adaptively Censored [PDF] from ajetr.org X Liu, K Wang, MP Zhu… – American Journal of Engineering and …, 2011 – ajetr.org … Fitzgerald[4] has shown that Hypothesis Pruning is an effective way to prevent track coalescence and the resulting exact nearest neighbor version of the JPDA (ENNPDA) uses the JPDA weight evaluation and subsequently prunes all Gaussians from the conditional density … Related articles – View as HTML – All 2 versions

Feature based similarity search with application to speedpath analysis N Callegari, LC Wang… – Test Conference, 2009. ITC …, 2009 – ieeexplore.ieee.org … Section 4 presents building a single model for speedpath or non-speedpath model- ing and how the model can be used to find potential speedpaths. Section 5 introduces Hypothesis Pruning and Ranking and how it is utilized for a similarity search. … Cited by 3 – Related articles

Subword-based automatic lexicon learning for Speech Recognition T Mertens… – Automatic Speech Recognition and …, 2011 – ieeexplore.ieee.org … 244 Page 3. Spellneme inventory Spellneme LM Word-level spellneme lexicon Spellneme decoding Hypothesis Pruning Word-level lexicon Pronuncia on Valida on Spellneme N- best output Rank Scoring Thresholding R Joint Valida on …

Multi-model hypothesis group tracking and group size estimation [PDF] from uni-freiburg.de B Lau, KO Arras… – International Journal of Social Robotics, 2010 – Springer Page 1. Int J Soc Robot (2010) 2: 19-30 DOI 10.1007/s12369-009-0036-0 Multi-model Hypothesis Group Tracking and Group Size Estimation Boris Lau · Kai O. Arras · Wolfram Burgard Accepted: 16 December 2009 / Published … Cited by 13 – Related articles – All 10 versions

Efficient radar data processing algorithm for dense cluttered environment J Guo… – Radar (Radar), 2011 IEEE CIE International …, 2011 – ieeexplore.ieee.org … For example, we can adaptively setting at most N hypotheses can be generated per track. 5. Adaptive track Hypothesis Pruning: when high clutter density, low track score hypotheses can be deleted straightway by adjusting the delete threshold in the track level pruning. …

Combining multiple scoring systems for target tracking using rank-score characteristics [PDF] from fordham.edu DM Lyons… – Information Fusion, 2009 – Elsevier … measurement. The results of this study will demonstrate that this diversity measure is a useful criterion for selecting fusion operations. 4. Target Hypothesis Pruning and feature selection. In … system. 4.1. Target Hypothesis Pruning. Hsu et al. [12 … Cited by 18 – Related articles – All 4 versions

Addressing the greedy-target problem in multiple-hypothesis tracking S Coraluppi… – Aerospace Conference, 2011 IEEE, 2011 – ieeexplore.ieee.org … programming formulation of the problem is due to Morefield [2]. The hybrid-state decomposition that allows for computationally efficient track-oriented MHT is due to Kurien [3]. An efficient solution to the optimization problem required for n-scan Hypothesis Pruning via Lagrangian … Cited by 2 – Related articles – All 3 versions

Multiple hypothesis tracking in microscopy images [PDF] from enst.fr N Chenouard, I Bloch… – … Imaging: From Nano to …, 2009 – ieeexplore.ieee.org … such complex problems. The computation is efficient because the algorithm design allows automatic and efficient Hypothesis Pruning and taking advan- tage of advanced parallel computing technologies. Our improved MHT … Cited by 7 – Related articles – All 6 versions

Localization with non-unique landmark observations N Özkucur… – RoboCup 2010: Robot Soccer World Cup XIV, 2011 – Springer … In this paper, we established the probabilistic model of the problem and showed the difficulty of optimal solution. After that, we introduce our importance sam- pling based approximate solution and implicit Hypothesis Pruning. … Cited by 6 – Related articles – All 4 versions

Automatic optimization of speech decoder parameters [PDF] from shef.ac.uk A El Hannani… – Signal Processing Letters, IEEE, 2010 – ieeexplore.ieee.org … Nevertheless these parameters are highly cor- related. For example, maximum Hypothesis Pruning is known to have a strong effect only if the main pruning beams are wide. Thus individual optimization of parameters is not useful. Fig. … Cited by 8 – Related articles – All 4 versions

Linking and building ontologies of linked data [PDF] from uzh.ch R Parundekar, C Knoblock… – The Semantic Web-ISWC 2010, 2010 – Springer … (rdf:type=lgd:node) (rdf:type=dbpedia:BodyOfWater & dbpedia:Place#type=dbpedia:City) (rdf:type=lgd:node) (dbpedia:Place#type=dbpedia:City) Seed Hypothesis Pruning (owl:Thing covers all instances) Prune as no change in the extension set Pruning on empty set r2=Ø … Cited by 9 – Related articles – All 7 versions

[PDF] Object constellations: Scalable, simultaneous detection and recognition of multiple specific objects [PDF] from ucla.edu SN Lim, G Doretto… – Workshop on Cognitive Vision in …, 2010 – vision.ucla.edu … Page 6. 6 Ser-Nam Lim1 Gianfranco Doretto2 Jens Rittscher1 Fig. 2. Object hypothesis pruning. … Finally, Fig- ure 5(c) shows a true negative example that can easily be rejected by the cutoff threshold. Object Hypothesis Pruning. … Cited by 1 – Related articles – View as HTML – All 7 versions

Performance of track formation algorithms using radar measurements influenced by urban environment W Buda, T Dorau, D Lukwinski… – Radar Symposium ( …, 2011 – ieeexplore.ieee.org … MHT track initiator characteristics: – state estimator – Kalman filter based on a simplified ballistic movement model [1], – filter parameterised with nominal standard deviations of measurement errors, – 4 steps deep MHT Hypothesis Pruning algorithm, – new tracks are validated … Related articles

Multiple hypothesis tracking for automated vehicle perception G Thomaidis, L Spinoulas, P Lytrivis… – … (IV), 2010 IEEE, 2010 – ieeexplore.ieee.org … Again, hypothesis matrices for each scan are concatenated to produce the multiple-scan hypothesis matrix. The number of scans for the N-scan sliding window Hypothesis Pruning was chosen , but in general this value varies depending on the complexity of the test case. … Cited by 2 – Related articles

A Library for Implementing the Multiple Hypothesis Tracking Algorithm [PDF] from arxiv.org DM Antunes, DM de Matos… – Arxiv preprint arXiv:1106.2263, 2011 – arxiv.org … Thus the application should provide a predicate function which accepts the facts and events of a cluster and asserts if that cluster can ever be required for hypothesis generation. If not, then best K Hypothesis Pruning is applied to the cluster, with K= 1. … Cited by 1 – Related articles – All 3 versions

Incremental decoding for phrase-based statistical machine translation [PDF] from aclweb.org B Sankaran, A Grewal… – … of the Joint Fifth Workshop on …, 2010 – dl.acm.org … with Moses. We now give a comparative note between our approach and the pruning strategy in regular beam search. Delaying the Hypothesis Pruning is the im- portant aspect in our approach to incremental de- coding. In the … Cited by 1 – Related articles – All 20 versions

Multiple hypothesis situation analysis support system prototype [PDF] from isif.org J Roy… – Information Fusion (FUSION), 2010 13th …, 2010 – ieeexplore.ieee.org Page 1. Multiple Hypothesis Situation Analysis Support System Prototype Jean Roy Intelligence and Information Section Defence R&D Canada – Valcartier Quebec, Qc, Canada Jean.Roy@drdc-rddc.gc.ca Alexandre Bergeron … Cited by 2 – Related articles – All 3 versions

Multi-agent plan recognition: Formalization and algorithms [PDF] from usm.edu B Banerjee, L Kraemer… – Proceedings of AAAI, 2010 – aaai.org … Simultaneous team assign- ment and behavior recognition from spatio-temporal agent traces. In Proceedings of AAAI conference. Sukthankar, G., and Sycara, K. 2008. Hypothesis Pruning and ranking for large plan recognition problems. In Proc. of AAAI. Tambe, M. 1997. … Cited by 6 – Related articles – All 5 versions

Room-structure estimation in Manhattan-like environments from dense 21/2D range data using minumum entropy and histograms S Olufs… – … of Computer Vision (WACV), 2011 IEEE …, 2011 – ieeexplore.ieee.org … voxel upsampling 2ms camera orientation estimation (50 par- ticles) 38ms 2D Histogram generation 1ms Mean shift clustering 5ms Edge Grouping with hysteresis 1ms Plane hypothesis generation 2ms Plane Hypothesis Pruning 21ms Sum 72ms … Related articles – All 3 versions

Multiple people activity recognition using MHT over DBN A Tolstikov, C Phua, J Biswas… – Toward Useful Services for …, 2011 – Springer … The presence of this kind of sensors is required since we want to keep tracking ID of the person doing activities. Information from these kind of sensors is also useful for Hypothesis Pruning. 2. Sensor values which can provide information related to only one person. … Related articles – All 3 versions

Random restarts in minimum error rate training for statistical machine translation [PDF] from aclweb.org RC Moore… – Proceedings of the 22nd International Conference …, 2008 – dl.acm.org … without adversely affecting the quality of the feature weights eventually produced. We tested two implementations of this type of Hypothesis Pruning. In the more conservative im- plementation, after each decoding iteration, we … Cited by 21 – Related articles – All 17 versions

Bearing-only target tracking using a bank of MAP estimators [PDF] from umn.edu GP Huang, KX Zhou, N Trawny… – … and Automation (ICRA …, 2011 – ieeexplore.ieee.org Page 1. Bearing-only Target Tracking using a Bank of MAP Estimators Guoquan P. Huang, Ke X. Zhou, Nikolas Trawny, and Stergios I. Roumeliotis Abstract- Nonlinear estimation problems, such as bearing- only tracking, are … Related articles – All 3 versions

Branch and Price for Multi-Agent Plan Recognition [PDF] from usm.edu B Banerjee… – Twenty-Fifth AAAI Conference on Artificial …, 2011 – aaai.org … generic) pricing problem for MAPR. We show experimentally that this approach outper- forms a recently proposed Hypothesis Pruning algorithm in two domains: multi-agent blocks word, and intrusion detec- tion. The key benefit of … Related articles – All 4 versions

Generalised Covariance Union: A Unified Approach to Hypothesis Merging in Tracking [PDF] from ox.ac.uk S Reece… – Aerospace and Electronic Systems, …, 2010 – ieeexplore.ieee.org … corresponding to each hypothesis. Pruning discards the set of hypotheses that have insignificant probabilities, and keeps the remaining hypotheses [4]. Merging joins selected hypotheses together into a single consistent estimate. … Cited by 1 – Related articles – All 6 versions

Detection-guided multi-target Bayesian filter Y Wang, Z Jing, S Hu… – Signal Processing, 2011 – Elsevier Related articles – All 3 versions

Search Performance of Multi-Agent Plan Recognition in a General Model [PDF] from usm.edu B Banerjee… – Workshops at the Twenty-Fourth AAAI …, 2010 – aaai.org Page 1. Search Performance of Multi-Agent Plan Recognition in a General Model Bikramjit Banerjee and Landon Kraemer School of Computing The University of Southern Mississippi 118 College Drive #5106 Hattiesburg, MS … Related articles – All 4 versions

Coupled object detection and tracking from static cameras and moving vehicles [PDF] from ethz.ch B Leibe, K Schindler, N Cornelis… – Pattern Analysis and …, 2008 – ieeexplore.ieee.org … time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative Hypothesis Pruning. We evaluate … Cited by 89 – Related articles – All 25 versions

[PDF] Automatic chord recognition based on probabilistic integration of chord transition and bass pitch estimation [PDF] from ismir.net K Sumi, K Itoyama, K Yoshii… – Proceedings of the …, 2008 – ismir2008.ismir.net … For each unit between one and eight beats, 48 hy- potheses are generated for 48 chord symbols. 3.7.2 Hypothesis Pruning We use a beam search to prune the expanded hypotheses; this prevents the number of hypotheses from expanding ex- ponentially. … Cited by 16 – Related articles – View as HTML – All 6 versions

A unified Bayesian framework for global localization and SLAM in hybrid metric/topological maps S Tully, G Kantor… – The International Journal of Robotics …, 2012 – ijr.sagepub.com Page 1. A unified Bayesian framework for global localization and SLAM in hybrid metric/topological maps The International Journal of Robotics Research 31(3) 271-288 (c) The Author(s) 2012 Reprints and permission: sagepub … Related articles – All 2 versions

Classification rule learning using subgroup discovery of cross-domain attributes responsible for design-silicon mismatch [PDF] from unm.edu N Callegari, DG Drmanac, LC Wang… – Proceedings of the 47th …, 2010 – dl.acm.org … The methodology [1] Hypothesis Pruning and Ranking (HPR) is only able to identify this region given the following constraints. … 7. REFERENCES [1] N. Callegari, P. Bastani, L. Wang, “Speedpath analysis based on Hypothesis Pruning and ranking,” Proc. DAC 2009. … Cited by 4 – Related articles – All 3 versions

Object detection and tracking for autonomous navigation in dynamic environments [PDF] from uni-freiburg.de A Ess, K Schindler, B Leibe… – The International Journal …, 2010 – ijr.sagepub.com Page 1. Object Detection and Tracking for Autonomous Navigation in Dynamic Environments Andreas Ess 1 , Konrad Schindler 2 , Bastian Leibe 3 and Luc Van Gool 4 Abstract We address the problem of vision-based navigation … Cited by 15 – Related articles – All 8 versions

Fusion gain in multi-target tracking [PDF] from isif.org S Coraluppi, M Guerriero… – … Fusion (FUSION), 2010 …, 2010 – ieeexplore.ieee.org … s =2m, 1.5m, or 1m We base our output tracker performance statistics on the output of a multi-hypothesis tracker that includes logic- based track management and track-oriented hypothesis management with a linear-programming (LP) relaxation approach to hypothesis … Cited by 1 – Related articles – All 3 versions

An unsupervised boosting technique for refiningword alignment S Ananthakrishnan, R Prasad… – … Workshop (SLT), 2010 …, 2010 – ieeexplore.ieee.org … The comparison of decoding speeds is also summarized in Table 2. Using identical hypothesis pruning settings, decoding speed increased from 52.6 words/second to 57.2 words/second (an in- crease of 8.7%) for E2P, and from 50.4 words/second to 54.9 words/second (an 8.9 … Cited by 1 – Related articles

Tracking algorithm using N-back scan MHT under dense environments Y Obata, M Ito… – SICE Annual Conference, 2008, 2008 – ieeexplore.ieee.org … 2. OUTLINE OF THE METHOD 2.1 Hypothesis Pruning of MHT The MHT can maintain only limited number of hypotheses because computational time must be within the limited interval. As one of conventional Hypothesis Pruning … Related articles

[PDF] Multiple Maneuvering Targets Tracking Using Kalman and Real-Time Particle Filter A Comparison [PDF] from ijetch.org S Vasuhi, V Vaidehi… – International Journal of …, 2009 – ijetch.org Page 1. International Journal of Engineering and Technology Vol. 1, No.3, August, 2009 ISSN: 1793-8236 – 224 – Abstract – In this paper, a comparison between thetwo algorithms for tracking multiple maneuvering targets in heavy clutter is done. … Related articles – View as HTML

Multiple maneuvering target tracking using MHT and nonlinear non-gaussian kalman filter P Muthumanikandan, S Vasuhi… – … and Networking, 2008. …, 2008 – ieeexplore.ieee.org … Thus, the probability of a track is the sum of all hypotheses that contain the track. The number of tracks must be controlled. Thus, standard track and Hypothesis Pruning methods are utilized. Also, similar tracks are merged. The … Cited by 8 – Related articles

Activity Recognition for Dynamic Multi-Agent Teams [PDF] from ucf.edu G Sukthankar… – ACM Transactions on Intelligent Systems and …, 2011 – dl.acm.org … scenarios. In the simplest case, where all of the agents are members This article is an extended version of of the AAAI 2008 Conference paper “Hypothesis Pruning and Ranking for Large Plan Recognition Problems”. This research … Related articles – All 5 versions

Generalizations of Blom And Bloem’s PDF decomposition for permutation-invariant estimation [PDF] from 140.124.72.88 DF Crouse, P Willett… – Acoustics, Speech and …, 2011 – ieeexplore.ieee.org … The notion is that in re- moving all information about the target identities in each per- mutation family, one will remove the symmetry that can cause track coalescence. This is similar to the Hypothesis Pruning that occurs in the JPDAF* [1]. 3.3. … Cited by 5 – Related articles – All 3 versions

Scissors Branch Algorithms Application in Platform of Milk Cow Disease Diagnose Expert System Pronunciation W Li-shu, Y Guang-lin, L Zai-hua… – Computer Science and …, 2009 – ieeexplore.ieee.org … Considered the balance of optimization and efficiency, the search depth limited by machine’s spatial/time, the system Hypothesis Pruning depth threshold value is 5, when the depth surpasses 5, pruning will stop and it will infer on knowledge rule so as to obtain result directly. … All 2 versions

A Coarse-to-fine approach for fast deformable object detection [PDF] from pascal-network.org M Pedersoli, A Vedaldi… – Computer Vision and …, 2011 – ieeexplore.ieee.org … and in fact increases slightly, when the exact test procedure (DP) is substituted with the CF inference algorithm. This is probably due to the aggressive Hypothesis Pruning of the CF search which promotes less ambiguous detections. … Cited by 4 – Related articles – All 12 versions

Multi-view traffic sign detection, recognition, and 3d localisation [PDF] from kuleuven.be R Timofte, K Zimmermann… – Applications of Computer …, 2009 – ieeexplore.ieee.org … Visual consistency gives higher weight to pairs with the same basic shape. 5) Multi-view MDL Hypothesis Pruning – Minimum de- scription length is used to select the subset of 3D hypotheses which best explains the overall set of 2D candidates. … Cited by 14 – Related articles – All 13 versions

Comparison of fusion methods for multiple target tracking [PDF] from isif.org M Morelande… – Information Fusion (FUSION), 2010 …, 2010 – ieeexplore.ieee.org … new collection of sensor measurements. Many techniques, such as Hypothesis Pruning, track clustering and hypothesis merging, have been proposed to address this problem [1, 2]. 2.2 Monte Carlo MHT It is proposed to approximate … Related articles – All 3 versions

[PDF] Simplification Rules for Intuitionistic Propositional Tableaux [PDF] from acm.org M FERRARI, C FIORENTINI… – Submitted to ACM …, 2010 – tocl.acm.org Page 1. A Simplification Rules for Intuitionistic Propositional Tableaux MAURO FERRARI, Dipartimento di Informatica e Comunicazione, Universit`a degli Studi dell’Insubria CAMILLO FIORENTINI, Dipartimento di Scienze dell … Cited by 1 – Related articles – View as HTML – All 2 versions

[PDF] LITERATURE REVIEW CO-OPERATIVE MAPPING AND LOCALIZATION OF AUTONOMOUS ROBOTS [PDF] from ru.ac.za L DICKS – 2011 – cs.ru.ac.za … adequately spaced to prevent them being interpreted as a single landmark. How- ever, as described in [13], it is possible to localize with non-unique landmark observations by using an Importance Sampling based approximate solution and implicit Hypothesis Pruning. … Related articles – View as HTML

Improving cascade of classifiers by sliding window alignment in between [PDF] from cvut.cz K Zimmermann, D Hurych… – Automation, Robotics and …, 2011 – ieeexplore.ieee.org … an earlier detection stage. From the point of view of search space reduction our work may be compared with Felzenschwalb [15], where they use a partial hypothesis pruning with a sequence of thresholds. They show, that the … Cited by 1 – Related articles – All 3 versions

Binaural tracking of multiple moving sources [PDF] from ohio-state.edu N Roman… – Audio, Speech, and Language …, 2008 – ieeexplore.ieee.org … In gen- eral, optimum MHT and Bayesian solutions require an expo- nential number of evaluations and therefore are deemed im- practical [4]. Hypothesis Pruning and merging techniques have been proposed to reduce this computational burden, including measurement gating … Cited by 41 – Related articles – BL Direct – All 19 versions

[PDF] Track fusion with feedback for local trackers using MHT [PDF] from 144.206.159.178 D Danu, T Lang… – Proceedings of SPIE, the …, 2008 – 144.206.159.178 … Canada ABSTRACT With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution; however a good Hypothesis Pruning method is mandatory for efficient implementation. The … Cited by 1 – Related articles – View as HTML – All 5 versions

A symbol graph based handwritten math expression recognition [PDF] from usf.edu Y Shi… – Pattern Recognition, 2008. ICPR 2008. 19th …, 2008 – ieeexplore.ieee.org … then the survived full symbol sequences for each segmentation are always fewer than N. Table 1 gives the percentage of average symbol can- didate saturation and the average expression processing time in second with respect to the partial Hypothesis Pruning threshold in the … Related articles – All 5 versions

Range-Based People Detection and Tracking for Socially Enabled Service Robots OM Mozos, D Meyer-Delius… – Towards Service Robots …, 2012 – books.google.com Page 232. Range-Based People Detection and Tracking for Socially Enabled Service Robots Kai O. Arras, Boris Lau, Slawomir Grzonka, Matthias Luber, Oscar Martinez Mozos, Daniel Meyer-Delius, and Wolfram Burgard Abstract. …

Monaural Speech Segregation by Integrating Primitive and Schema-Based Analysis [PDF] from dtic.mil DL Wang, N Roman, G Hu, S Srinivasan… – 2008 – DTIC Document … Thus we propose a Hypothesis Pruning algorithm to remove hypotheses of low likelihoods, which drastically reduces computation time while resulting in comparable performance with exhaustive search. As a byproduct, speaker identities are also determined. … Library Search – All 3 versions

Range-Based People Detection and Tracking for Socially Enabled Service Robots K Arras, B Lau, S Grzonka, M Luber… – … Service Robots for …, 2012 – Springer Page 1. Range-Based People Detection and Tracking for Socially Enabled Service Robots Kai O. Arras, Boris Lau, Slawomir Grzonka, Matthias Luber, Oscar Martinez Mozos, Daniel Meyer-Delius, and Wolfram Burgard Abstract. … Cited by 1

[PDF] Lidar Signal Processing Techniques [PDF] from diva-portal.org T Sjöstedt – 2011 – kth.diva-portal.org Page 1. Lidar Signal Processing Techniques Clutter Suppression, Clustering and Tracking THEODOR SJÖSTEDT Master’s Degree Project Stockholm, Sweden May 2011 XR-EE-SB 2011:010 Page 2. Page 3. LIDAR SIGNAL PROCESSING TECHNIQUES: … Cited by 1 – Related articles – View as HTML – All 2 versions

Tracking of spawning targets with multiple finite resolution sensors [PDF] from isif.org H Chen, T Kirubarajan… – Aerospace and Electronic …, 2008 – ieeexplore.ieee.org … Recent works in [7], [8] show that the track coalescence problem can be avoided through coupled JPDA filter with Hypothesis Pruning. … References [15], [16] treat unresolved measurements in the context of the MHT and enumerate all the possibilities with Hypothesis Pruning. … Cited by 4 – Related articles – All 8 versions

Evaluation of a WFST-based ASR system for train timetable information [PDF] from hokudai.ac.jp JR Novak, EWD Whittaker… – … : APSIPA ASC 2009: …, 2009 – eprints.lib.hokudai.ac.jp … The development set, which consisted of 474 of the recorded utterances, was used to tune the decoder configu- ration, the parameters of which included an insertion penalty, a language model weight, and two parameters which are used to specify Hypothesis Pruning thresholds … Cited by 1 – Related articles – All 6 versions

Multiple target tracking with lazy background subtraction and connected components analysis [PDF] from unm.edu RG Abbott… – Machine Vision and Applications, 2009 – Springer Page 1. Machine Vision and Applications (2009) 20:93-101 DOI 10.1007/s00138-007-0109- 8 ORIGINAL PAPER Multiple target tracking with lazy background subtraction and connected components analysis Robert G. Abbott · Lance R. Williams … Cited by 6 – Related articles – All 13 versions

First molecular estimate of cyclostome bryozoan phylogeny confirms extensive homoplasy among skeletal characters used in traditional taxonomy A Waeschenbach, CJ Cox, DTJ Littlewood… – Molecular phylogenetics …, 2009 – Elsevier Cited by 11 – Related articles – All 9 versions

Multi-object tracking using semantic analysis and Kalman filter SS Pathan, A Al-Hamadi, T Senst… – Image and Signal …, 2009 – ieeexplore.ieee.org … situations is proposed. The propagation of flow path is based on our maximum likelihood and logical con- straints. The search space criterion and logical constraints are analogous to Hypothesis Pruning in this work. Thus, we are … Cited by 2 – Related articles

OIF-An Online Inferential Framework for Multi-object Tracking with Kalman Filter S Pathan, A Al-Hamadi… – Computer Analysis of Images and …, 2009 – Springer … The propagation of flow path is based on maximum likelihood and integrity constraints. The search space criteria and in- tegrity constraints are analogous to Hypothesis Pruning in this work. Thus, we Page 3. OIF – An Online Inferential Framework 1089 Fig. … Cited by 1 – Related articles – All 3 versions

[PDF] COOPERATIVE LOCALIZATION AND MAPPING OF AUTONOMOUS ROBOTS [PDF] from ru.ac.za L Dicks – 2011 – cs.ru.ac.za … spaced to prevent them being interpreted as a single landmark. However, as described in [18], it is possible to localize with non-unique landmark observations by using an importance sampling based approximate solution and implicit Hypothesis Pruning. This … Related articles – View as HTML – All 2 versions

Information theoretic measures for performance evaluation and comparison [PDF] from dtic.mil H Chen, G Chen, EP Blasch… – … , 2009. FUSION’09. …, 2009 – ieeexplore.ieee.org Page 1. Information Theoretic Measures for Performance Evaluation and Comparison Huimin Chen Department of Electrical Engineering University of New Orleans New Orleans, LA 70148, USA hchen2@uno.edu Genshe Chen … Cited by 4 – Related articles – All 6 versions

Combination of error detection techniques in automatic speech transcription K Abida, W Abida… – Autonomous and Intelligent Systems, 2011 – Springer … In large vocabulary speech transcription, recognition errors can be caused by a variety of factors[1]. The most common of these causes are: missing terms from the language model, Hypothesis Pruning during the Viterbi decoding procedure, noisy environment, etc. … Cited by 1 – Related articles – All 3 versions

Smoothed state estimation for nonlinear Markovian switching systems MR Morelande… – Aerospace and Electronic Systems, …, 2008 – ieeexplore.ieee.org … measurement equations. Three algorithms for smoothing are proposed in this context. The first two algorithms are based on the well-known interacting multiple model (IMM) and Hypothesis Pruning techniques. The third algorithm … Cited by 4 – Related articles – All 2 versions

Multiple Detection Probabilistic Data Association filter for multistatic target tracking BK Habtemariam, R Tharmarasa… – … Proceedings of the …, 2011 – ieeexplore.ieee.org … Although it is an optimal approach, within few steps it will become computationally infeasible. Hypothesis Pruning techniques can be applied to the MHT approach for practical problems at the expense of optimality [10]. Another … Related articles

Globally optimal solution to multi-object tracking with merged measurements [PDF] from uc.pt JF Henriques, R Caseiro… – Computer Vision (ICCV), …, 2011 – ieeexplore.ieee.org … Their main contribution is an iterative procedure to solve occlusions. The authors of [13] go further and use Quadratic Boolean Programming, but this class of problems cannot be solved without Hypothesis Pruning, which may yield sub-optimal solutions. … Related articles – All 8 versions

Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms F Bingyi… – Journal of Systems Engineering and Electronics, 2008 – Elsevier … In this approach the priori probbility that the target measurements will get merged is calculated. A very similar approach was adopted in Ref. [5], which treat unresolved measurements in the context of the MHT and enumerate all the possibilities with Hypothesis Pruning. … Related articles – All 5 versions

Multi-target/multi-sensor tracking using only range and doppler measurements [PDF] from dtic.mil R Deming, J Schindler… – Aerospace and Electronic …, 2009 – ieeexplore.ieee.org … and are both based upon EM. For multi-sensor, multi-target applications, MHT and JPDA would likely require Hypothesis Pruning in order to avoid an exponential increase in computational cost. Since the computational cost of … Cited by 18 – Related articles – Library Search – All 11 versions

A theoretical analysis of Bayes-optimal multi-target tracking and labelling [PDF] from utwente.nl EH Aoki, A Bagchi, PK Mandal… – 2011 – eprints.eemcs.utwente.nl … mathematical characterization of the mixed labelling phenomenon, explain its physical interpre- tation, and its consequences to track extraction; 3) We describe the “self-resolving” property of particle filters (and other approaches based on Hypothesis Pruning), with emphasis on … Cited by 2 – Related articles – All 3 versions

EMG-based speech recognition using hidden Markov models with global control variables KS Lee – Biomedical Engineering, IEEE Transactions on, 2008 – ieeexplore.ieee.org … The value of 0.637 ( ) was obtained from Table VI. This value does not clearly reject the null hypothesis (pruning and nonpruning training methods are independent in terms of the average WRR). A possible explanation for this result is that the average … Cited by 15 – Related articles – BL Direct – All 5 versions

Improving face segmentation in thermograms using image signatures [PDF] from ubi.pt S Filipe… – Progress in Pattern Recognition, Image Analysis, …, 2010 – Springer … Face recog- nition in thermal infrared. ch. 29, pp. 1-15. CRC Press, Boca Raton (2006) 9. Srivastava, A., Liu, X.: Statistical Hypothesis Pruning for identifying faces from infrared images. Image and Vision Computing, 651-661 Related articles – All 6 versions

[PDF] Syntax-based Language Models for Statistical Machine Translation [PDF] from rochester.edu MJ Post – 2010 – cs.rochester.edu Page 1. Syntax-based Language Models for Statistical Machine Translation by Matthew John Post Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Supervised by Professor Daniel J. Gildea … Cited by 2 – Related articles – View as HTML – Library Search

Fault detection model-based controller for process systems VT Minh, N Afzulpurkar… – Asian Journal of …, 2011 – Wiley Online Library … Download figure to PowerPoint. thumbnail image. The GPB1 and GPB2 algorithms are the result of early work by Ackerson and Fu 25, and good overviews are provided in 26, where suboptimal Hypothesis Pruning techniques are compared. … Related articles – All 2 versions

Post-silicon failing-test generation through evolutionary computation [PDF] from polito.it E Sanchez, G Squillero… – VLSI and System-on-Chip ( …, 2011 – ieeexplore.ieee.org … Conference, 2007, pp. 390-395. [6] N. Callegari, L. .-C. Wang, and P. Bastani, “Speedpath analysis based on Hypothesis Pruning and ranking,” in 46th ACM/IEEE Design Automation Conference, 2009, pp. 346-351. [7] L. Lee … Cited by 2 – Related articles – All 2 versions

Generalised Interaction Mining: Probabilistic, Statistical and Vectorised Methods in High Dimensional or Uncertain Databases [PDF] from uni-muenchen.de F Verhein – 2010 – edoc.ub.uni-muenchen.de Page 1. Generalised Interaction Mining: Probabilistic, Statistical and Vectorised Methods in High Dimensional or Uncertain Databases Florian Verhein Dr. rer. nat. Dissertation Faculty of Mathematics, Informatics and Statistics … Related articles – Library Search – All 4 versions

Quo vadis face recognition: Spectral considerations [PDF] from montclair.edu SA Robila – … and Technology Conference, 2009. LISAT’09. …, 2009 – ieeexplore.ieee.org … of the IEEE, Vol. 94, No. 11, pp. 1948-1962, 2006 [4] A. Srivastava and X. Liu, “Statistical Hypothesis Pruning for identifying faces from infrared images, Image and Vision Computing”, Computer Vision beyond the visible spectrum, vol 21, no. 7, pp. … Related articles – All 2 versions

[PDF] Logic-based approaches to intention recognition [PDF] from missouri.edu F Sadri – Handbook of Research on Ambient Intelligence: …, 2010 – vigir.missouri.edu Page 1. Logic-Based Approaches to Intention Recognition Fariba Sadri Department of Computing, Imperial College London, UK fs@doc.ic.ac.uk Abstract In this paper we discuss intention recognition in general, and the use … Cited by 1 – Related articles – View as HTML – All 8 versions

[PDF] New algorithms and hardness results for multi-agent plan recognition [PDF] from strath.ac.uk B Banerjee, J Lyle… – GAPRec 2011, 2011 – personal.cis.strath.ac.uk Page 32. New Algorithms and Hardness Results for Multi-Agent Plan Recognition Bikramjit Banerjee School of Computing The University of Southern Mississippi Hattiesburg, MS 39406 Jeremy Lyle Dept. of Mathematics The … Related articles – View as HTML – All 11 versions

Low-complexity receivers for multiuser detection with an unknown number of active users [PDF] from unicas.it D Angelosante, E Biglieri… – Signal Processing, 2010 – Elsevier Cited by 9 – Related articles – All 11 versions

Automatic generation of software-based functional failing test for speed debug and on-silicon timing verification [PDF] from polito.it E Sanchez, G Squillero… – Microprocessor Test and …, 2011 – ieeexplore.ieee.org … Conference, 2007, pp. 390- 395. [3] N. Callegari, L. .-C. Wang, and P. Bastani, “Speedpath analysis based on Hypothesis Pruning and ranking,” in 46th ACM/IEEE Design Automation Conference, 2009, pp. 346-351. [4] J. Zeng … Related articles – All 4 versions

[PDF] Efficient Multi-Target Tracking Using Graphical Models [PDF] from mit.edu ZM Chen – 2008 – ssg.mit.edu Page 1. Efficient Multi-Target Tracking Using Graphical Models by Zhexu (Michael) Chen SB in Electrical Engineering and Computer Science SB in Management Science Massachusetts Institute of Technology, 2008 Submitted … Cited by 2 – Related articles – View as HTML – All 4 versions

Efficient Multi-Target Tracking using graphical models [PDF] from 18.7.29.232 AS Willsky, ZZM Chen – 2008 – 18.7.29.232 Page 1. Efficient Multi-Target Tracking Using Graphical Models by Zhexu (Michael) Chen SB in Electrical Engineering and Computer Science SB in Management Science Massachusetts Institute of Technology, 2008 Submitted … Related articles – All 2 versions

[PDF] Plan Recognition and Tracking for Cooperative Autonomous Robots in Dynamic Environments [PDF] from das-lab.net S Triller, A Zündorf… – 2010 – carpenoctem.das-lab.net Page 1. Plan Recognition and Tracking for Cooperative Autonomous Robots in Dynamic Environments University of Kassel Master Thesis of Stefan Triller At Distributed Systems Group Reviewer: Prof. Dr. Kurt Geihs Prof. Dr. Albert Zündorf Supervisor: Dipl.-Inf. Hendrik Skubch … Related articles – View as HTML – All 2 versions

[PDF] Random finite sets in multi-object filtering [PDF] from hw.ac.uk BT Vo – 2008 – randomsets.eps.hw.ac.uk Page 1. Random Finite Sets in Multi-Object Filtering Ba Tuong Vo This dissertation is presented for the degree of Doctor of Philosophy School of Electrical, Electronic and Computer Engineering THE UNIVERSITY OF WESTERN AUSTRALIA October 2008 Page 2. … Cited by 23 – Related articles – View as HTML – All 5 versions

Automatic features identification with Infrared Thermography in Fever Screening [PDF] from uottawa.ca V Surabhi – 2012 – ruor.uottawa.ca Page 1. Automatic features identification with Infrared Thermography in Fever Screening by Vijaykumar Surabhi The thesis submitted to the Faculty of Graduate and Postdoctoral Studies In Partial Fulfilment of the Requirements For the Degree of … Related articles – All 2 versions

[PDF] 2D-Multiwavelet Transform 2D-Two Activation Function Wavelet Network Based Face Recognition [PDF] from aensionline.com WA Mahmoud, ME Alneby… – Journal of Applied …, 2010 – aensionline.com Page 1. Journal of Applied Sciences Research, 6(8): 1019-1028, 2010 (c) 2010, INSInet Publication 2D-Multiwavelet Transform 2D-Two Activation Function Wavelet Network Based Face Recognition Walid A. Mahmoud, Majed E. Alneby and Wael H. Zayer … Related articles – View as HTML – All 2 versions

[PDF] Investigating face recognition from hyperspectral data: impact of band extraction [PDF] from 144.206.159.178 SA Robila, A LaChance… – Algorithms and Technologies …, 2009 – 144.206.159.178 … terrorists”, IEEE Spectrum 44(7), 47-52 (2007). [7] A. Srivastava, X. Liu, “Statistical hypothesis pruning for identifying faces from infrared images, Image and Vision Computing”, Computer Vision Beyond the Visible Spectrum 21(7), 651-661 (2003). … Cited by 2 – Related articles – View as HTML – All 5 versions

Recognition of overlapping objects. [PDF] from uottawa.ca TR Damerji – 2009 – ruor.uottawa.ca … 53 4.3.1 Hypothesis Generation . . . . . 53 4.3.2 AR classi?ers . . . . . 5-1 4.3.3 Hypothesis Pruning . . . . . 57 4.3.4 Hypothesis Veri?cation . . . . . 58 4.4 Conclusion . . . . . … Related articles – Library Search – All 2 versions

A Gaussian Mixture Filter for Range-Only Tracking JMC Clark, PA Kountouriotis… – Automatic Control, IEEE …, 2011 – ieeexplore.ieee.org Page 1. Copyright (c) 2010 IEEE. Personal use is permitted. For any other purposes, Permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. … Related articles – All 4 versions

[PDF] Toward hyperspectral face recognition [PDF] from 144.206.159.178 SA Robila – Proceedings of SPIE-IS&T Electronic Imaging, 2008 – 144.206.159.178 … pp. 399-458 7 A. Srivastava and X. Liu, Statistical Hypothesis Pruning for identifying faces from infrared images, Image and Vision Computing, vol. 21, Issue 7, , Computer Vision beyond the visible spectrum, 2003, pp. 651-661. … Cited by 5 – Related articles – View as HTML – All 6 versions

[PDF] Multi-sensor based Localization and Tracking for Intelligent Environments [PDF] from utl.pt DMGBR Antunes – 2011 – dspace.ist.utl.pt Page 1. Multi-sensor based Localization and Tracking for Intelligent Environments David Miguel Guilherme Branquinho Ribeiro Antunes Dissertaç˜ao para obtenç˜ao do Grau de Mestre em Engenharia Informática e de Computadores Júri Presidente: Prof. … Related articles – View as HTML

Multihypothesis Viterbi Data Association: Algorithm Development and Assessment [PDF] from pagesperso-orange.fr GW Pulford… – Aerospace and Electronic Systems, …, 2010 – ieeexplore.ieee.org Page 1. Multihypothesis Viterbi Data Association: Algorithm Development and Assessment GW PULFORD, Senior Member, IEEE QinetiQ UK BF LA SCALA National Australia Bank Two algorithms for tracking in clutter, based … Cited by 4 – Related articles – All 6 versions

[PDF] What You See Is What You Get so Look At What You Want To See [PDF] from cmu.edu M Desnoyer – 2011 – cs.cmu.edu Page 1. May 31, 2011 DRAFT What You See Is What You Get so Look At What You Want To See Assessing the Impact of Visual Utility Based Camera Control Mark Desnoyer May 2011 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 … Related articles – View as HTML

[PDF] Multitarget multisensor tracking in the presence of wakes [PDF] from isif.org A Rødningsby, Y BAR-SHALOM… – in Journal of Advances …, 2009 – isif.org Page 1. Multitarget Multisensor Tracking in the Presence of Wakes ANDERS RØDNINGSBY YAAKOV BAR-SHALOM ODDVAR HALLINGSTAD JOHN GLATTETRE In this paper we focus on targets which, in addition to reflecting … Cited by 1 – Related articles – View as HTML – All 3 versions

[BOOK] Cooperative Anchoring: Sharing Information about Objects in Multi-robotsystems [PDF] from oru.se K LeBlanc – 2010 – aass.oru.se Page 1. Doctoral Dissertation Cooperative Anchoring: Sharing Information about Objects in Multi-Robot Systems Kevin LeBlanc Technology Örebro Studies in Technology 39 örebro 2010 Page 2. Page 3. Cooperative Anchoring: Sharing Information about Objects … Related articles – View as HTML – Library Search – All 6 versions

Multi-Agent Plan Recognition with Partial Team Traces and Plan Libraries [PDF] from zsusoft.com HH Zhuo… – Twenty-Second International Joint Conference on …, 2011 – aaai.org Page 1. Multi-Agent Plan Recognition with Partial Team Traces and Plan Libraries Hankz Hankui Zhuo and Lei Li Department of Computer Science, Sun Yat-sen University, Guangzhou, China {zhuohank,lnslilei}@mail.sysu.edu.cn Abstract … Related articles – All 6 versions

On Signal Tracing for Debugging Speedpath-Related Electrical Errors in Post-Silicon Validation [PDF] from cuhk.edu.hk X Liu… – Test Symposium (ATS), 2010 19th IEEE Asian, 2010 – ieeexplore.ieee.org … http://www.arm.com/. [6] N. Callegari, LC Wang, and P. Bastani. Speedpath Analysis Based on Hypothesis Pruning and Ranking. In Proceedings ACM/IEEE Design Automation Conference (DAC), pages 346-351, 2009. [7] ABT Hopkins and KD McDonald-Maier. … Related articles – All 8 versions

[BOOK] A computational perspective on visual attention JK Tsotsos – 2011 – books.google.com Page 1. A Computational Perspective on Visual Attention John K Tsotsos Page 2. A Computational Perspective on Visual Attention Page 3. Page 4. A Computational Perspective on Visual Attention John K. Tsotsos The MIT Press Cambridge, Massachusetts London, England … Cited by 11 – Related articles – Library Search – All 4 versions

[PDF] Face recognition beyond the visible spectrum [PDF] from psu.edu P Buddharaju, I Pavlidis… – Advances in Biometrics: Sensors, …, 2008 – Citeseer Page 1. Face Recognition Beyond the Visible Spectrum Pradeep Buddharaju1, Ioannis Pavlidis1, and Chinmay Manohar2 1 Dept. of Computer Science, University of Houston 4800 Callhoun Road, Houston, TX 77204 braju@cs … Cited by 4 – Related articles – View as HTML – All 6 versions

Thermal and reflectance based personal identification methodology under variable illumination [PDF] from cam.ac.uk O Arandjelovic, R Hammoud… – Pattern Recognition, 2010 – Elsevier Related articles – All 8 versions

[PDF] Detection of Suspicious Behavior from a Sparse Set of Multiagent Interactions [PDF] from usc.edu B Kaluža, GA Kaminka… – 2012 – teamcore.usc.edu Page 1. Detection of Suspicious Behavior from a Sparse Set of Multiagent Interactions Boštjan Kaluža Jozef Stefan Institute Ljubljana, Slovenia bostjan.kaluza@ijs.si Gal A. Kaminka Bar Ilan University Ramat Gan, Israel galk@cs.biu.ac.il … Related articles – View as HTML

Mining AC delay measurements for understanding speed-limiting paths [PDF] from unm.edu J Chen, B Bolin, LC Wang, J Zeng… – … (ITC), 2010 IEEE …, 2010 – ieeexplore.ieee.org Page 1. Mining AC Delay Measurements for Understanding Speed-limiting Paths * Janine Chen1,2, Brendon Bolin1, Li-C. Wang1, Jing Zeng2, Dragoljub (Gagi) Drmanac1, Michael Mateja2 1Department of ECE, UC-Santa Barbara; 2Advanced Micro Devices, Inc. Abstract … Cited by 1 – Related articles – All 2 versions

Perception de la géométrie de l’environnement pour la navigation autonome [PDF] from ups-tlse.fr C Berger – 2009 – thesesups.ups-tlse.fr Page 1. THÈSE En vue de l’obtention du DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE Délivré par l’Université Toulouse III – Paul Sabatier Discipline ou spécialité : Systèmes embarquées JURY Walter Mayol, Rapporteur Fawzi … Related articles – All 6 versions

Combination of faces recognition techniques in infrared images by using genetic algorithms D Martinez Torres, E Caicedo Bravo… – … , IEEE (Revista IEEE …, 2008 – ieeexplore.ieee.org … Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications. 2001. [2] A. Srivastava, X. Liu. “Statistical Hypothesis Pruning for Identifying Faces From Infrared Images”. Journal of Image and Vision Computing, vol. 21, no. 7, pp. … Cited by 3 – Related articles – All 2 versions

[PDF] Discovering context changes in team behavior [PDF] from usukita.org SE Poltrock, M Handel, H Bowyer… – Proceedings of the 3rd …, 2009 – usukita.org … [7] Sukthankar, G., Sycara, K (2008). “Hypothesis Pruning and Ranking for Large Plan Recognition Problems,” Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08), July 13-16, Chicago, IL, 2008. … Cited by 2 – Related articles – View as HTML – All 4 versions

Artificial evolution in computer aided design: from the optimization of parameters to the creation of assembly programs [PDF] from polito.it G Squillero – Computing, 2011 – Springer Page 1. Computing (2011) 93:103-120 DOI 10.1007/s00607-011-0157-9 Artificial evolution in computer aided design: from the optimization of parameters to the creation of assembly programs Giovanni Squillero Received: 26 … Related articles – All 5 versions