Notes:
A linear classifier is a type of machine learning algorithm that assigns input data to one of several predefined classes or categories. It is a simple and efficient algorithm that uses a linear combination of the input features to make predictions about the class of the data.
Linear classifiers can be used in dialog systems to help the system understand the meaning of user input and generate appropriate responses. In a dialog system, the input data could be the words or phrases used by the user, and the classes could be different categories of meaning or intent. For example, the classes could be questions, statements, requests, or commands, and the linear classifier could be used to predict which of these classes the user’s input belongs to.
Once the linear classifier has predicted the class of the user’s input, the dialog system can use this information to generate an appropriate response. For example, if the linear classifier predicts that the user’s input is a question, the dialog system could generate a response that answers the question. This can help the dialog system to generate more accurate and relevant responses to the user’s input, and to improve the overall quality of the conversation.
Overall, linear classifiers are simple and efficient algorithms that can be used in dialog systems to help the system understand the meaning of user input and generate appropriate responses. They can provide a fast and accurate way of predicting the class of the user’s input, and they can help the dialog system to generate more relevant and accurate responses.
Wikipedia:
See also:
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[PDF] Machine Learning in Speech and Language Processing [PDF] from att.com JA Bilmes… – Proc. ICASSP, 2005 – research.att.com … Input Space f Preprocessing Linear Classifier Avoid preprocessing: Work in input space? Expensive … Making kernels 14 Page 15. Example with Polynomial Kernels Imagine a linear classifier over all pairs of input features: ” d(d + 1)/2 features. ” Dot product in Hilbert space: O( … Cited by 3 – Related articles – View as HTML – All 4 versions
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Using graphone models in automatic speech recognition [PDF] from mit.edu JR Glass, IL Hetherington, SX Wang – 2009 – dspace.mit.edu … This often results in user frustration and reduced user confidence in speech dialogue systems in general. … Multiple confidence measures can also be combined to construct confidence vectors, which are then passed through a linear classifier to determine if the corresponding … Cited by 7 – Related articles – Library Search – All 3 versions
[PDF] Exploring Biologically-Inspired Interactive Networks for Object Recognition [PDF] from diva-portal.org M Saifullah – 2011 – liu.diva-portal.org Page 1. Linköping Studies in Science and Technology Thesis No. 1466 Exploring Biologically-Inspired Interactive Networks for Object Recognition by Mohammad Saifullah Submitted to Linköping Institute of Technology at Linköping … Cited by 1 – Related articles – View as HTML – All 2 versions
[PDF] Elements for Learning and Optimizing Expensive Fuctions [PDF] from lri.fr P Rolet – 2010 – lri.fr … This is an illustrative example; MDPs are of course suited to model many real-world processes, such as for instance: Dialogue systems An automated machine must interact with a user to acquire information and perform actions (eg interactive voice response for a … Cited by 1 – Related articles – View as HTML
[PDF] Kernels for Structured Data in Natural Language Processing [PDF] from aist-nara.ac.jp J Suzuki – Doctor Thesis, Nara Institute of Science and …, 2005 – cl.aist-nara.ac.jp Page 1. NAIST-IS-DD0361207 Doctoral Dissertation Kernels for Structured Data in Natural Language Processing Jun Suzuki March 24, 2005 Department of Information Processing Graduate School of Information Science Nara Institute of Science and Technology Page 2. … Cited by 2 – Related articles – View as HTML – All 4 versions
[PDF] A Machine Learning Approach to Anaphora Resolution Including Named Entity Recognition, PP Attachment Disambiguation, and Animacy Detection [PDF] from psu.edu A Nøklestad – 2009 – Citeseer Page 1. A Machine Learning Approach to Anaphora Resolution Including Named Entity Recognition, PP Attachment Disambiguation, and Animacy Detection Anders Nøklestad May 7, 2009 Page 2. 2 For my parents, Randi and Hans Olaf Page 3. Contents 1 Introduction 13 … Cited by 2 – Related articles – View as HTML – All 4 versions
Improvement of the Post-processing Method for Isolated Word OOV Rejection [PDF] from colips.org Y Zhu, C Li… – International Symposium on Chinese Spoken …, 2002 – isca-speech.org … First of all, by using a linear classifier, we combine several promising features presented by others to obtain a confidence measure … speech recognition applications, the rejection of out-of-vocabulary (OOV) words is an important issue, especially in dialogue systems, where acting … Related articles – All 4 versions
[PDF] Department oF Signal and Image Processing 46, rue Barrault Paris 75634 C’edex 13 France [PDF] from archives-ouvertes.fr P Verlinde – 2004 – hal.archives-ouvertes.fr … Neighbor NSA National Security Agency (USA) PE Profile Expert PIN Personal Identification Number PLC Piece-wise Linear Classifier QC Quadratic … shopping, accessing the safe room of your bank, tele-banking, accessing the services of interactive dialogue systems [175], or … Related articles – View as HTML
[BOOK] Code breaking for automatic speech recognition [PDF] from psu.edu V Venkataramani… – 2005 – Citeseer Page 1. Code Breaking for Automatic Speech Recognition Veera Venkataramani A dissertation submitted to the Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy. Baltimore, Maryland 2005 … Cited by 1 – Related articles – View as HTML – Library Search – All 8 versions
[PDF] Determining user state and mental task demand from electroencephalographic data [PDF] from uniklinik-freiburg.org M Honal – University of Karlsruhe, 2005 – uniklinik-freiburg.org Page 1. Determining User State and Mental Task Demand From Electroencephalographic Data Diplomarbeit Matthias Honal University of Karlsruhe Supervisors: Dr. Tanja Schultz Prof. Dr. Alexander Waibel November 2005 Page 2. Page 3. … Cited by 5 – Related articles – View as HTML – All 20 versions
[PDF] Mining Speech Sounds [PDF] from psu.edu G SALVI – KTH: Stockholm, 2006 – Citeseer … However, it is worth noting that many aspects of human-to-human interaction concern the way the speaker’s and listener’s roles are exchanged, and this picture would be misleading if the focus was on dialogue and dialogue systems research. … Related articles – View as HTML – All 9 versions
Computational Terminology: Exploring Bilingual and Monolingual Term Extraction [PDF] from diva-portal.org J Foo – 2012 – liu.diva-portal.org Page 1. Linköping Studies in Science and Technology. Thesis, No. 1523 Computational Terminology: Exploring Bilingual and Monolingual Term Extraction by Jody Foo Submitted to Linköping Institute of Technology at Linköping …
[PDF] Automatic voicemail summarisation for mobile messaging [PDF] from quid5.net K Koumpis – 2002 – server.quid5.net … PARADISE paradigm for dialogue system evaluation … 96 7.6 The ROC curves produced for the binary decision summarisation task using as inputs to a linear classifier the individual features of Table 7.2 with respect to four training sets containing different amount of data. . . . … Cited by 4 – Related articles – Library Search – All 5 versions