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Notes:

Classification algorithms are a type of machine learning algorithm that are used to predict the class or category to which a given input belongs. They are used to assign labels or categories to a set of data based on certain features or characteristics of that data. Classification algorithms can be used in a variety of applications, including spam detection, image classification, and fraud detection.

In dialog systems, classification algorithms can be used to classify and categorize user input in order to understand and respond appropriately. For example, a dialog system might use a classification algorithm to determine the intent or meaning of a user’s message and then generate a response based on that classification. Classification algorithms can be trained using labeled data, where the input data is associated with a specific class or category, and can be used to predict the class or category of new, unseen data.

There are many different types of classification algorithms, including decision trees, k-nearest neighbors, and support vector machines. The choice of algorithm will depend on the specific needs and goals of the dialog system, as well as the characteristics of the data being classified.

Wikipedia:

• Category:Classification algorithms

See also:

Best Dialog System Classifiers | Classifier & Dialog Systems | Learning Classifier & Dialog Systems | Linear Classifiers & Dialog Systems | Question Classifier Module | Stanford Classifier | Text Classification & Chatbots

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Popularity: (* may have another significance) [ 2002 – 2012 ]

• boosting (693)

• support vector machine (655)

• calibration (503)

• perceptron (461)

• case based reasoning (435)

• linear discriminant analysis (265)

• adaboost (255)

• artificial neural network (218)

• naive bayes classifier (177)

• kernel methods (161)

• multilayer perceptron (142)

• decision tree learning (123)

• statistical classification (90)

• co training (76)

• linear classifier (68)

• decision boundary (67)

• multiclass classification (47)

• generalization error (39)

• winnow (33)

• k nearest neighbor algorithm (31)

• random forest (30)

• c4 5 algorithm (29)

• syntactic pattern recognition (29)

• nearest neighbor search (27)

• conceptual clustering (26)

• ensemble learning (25)

• learning vector quantization (24)

• multi label classification (23)

• string kernel (21)

• information gain ratio (20)

• novelty detection (20)

• radial basis function network (18)

• margin classifier (17)

• id3 algorithm (15)

• relevance vector machine (14)

• multinomial logit (11)

• logitboost (8)

• alternating decision tree (6)

• random subspace method (6)

• quadratic classifier (5)

• analogical modeling (4)

• class membership probabilities (4)

• one class classification (4)

• averaged one dependence estimators (3)

• brownboost (3)

• chaid (3)

• elastic matching (3)

• least squares support vector machine (3)

• multiple discriminant analysis (3)

• locality sensitive hashing (2)

• types of artificial neural networks (2)

• cascading classifiers (1)

• compositional pattern producing network (1)

• gene expression programming (1)

• large margin nearest neighbor (1)

• textual case based reasoning (1)

• variable kernel density estimation (1)

• whitening transformation (1)

• alopex (0)

• classifier chains (0)

• coboosting (0)

• evolving classification function (0)

• feature selection toolbox (0)

• group method of data handling (0)

• idistance (0)

• information fuzzy networks (0)

• information gain in decision trees (0)

• monte carlo machine learning library (0)

• multifactor dimensionality reduction (0)

• multispectral pattern recognition (0)

• nearest centroid classifier (0)

• optimal discriminant analysis (0)

• random multinomial logit (0)

• soft independent modelling of class analogies (0)

• sukhotin’s algorithm (0)