About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 69. Chapters: Artificial neural network, Naive Bayes classifier, Support vector machine, Boosting, Linear classifier, Case-based reasoning, Radial basis function network, Types of artificial neural networks, Perceptron, Linear discriminant analysis, Least squares support vector machine, Nearest neighbor search, Locality sensitive hashing, Multifactor dimensionality reduction, Decision tree learning, K-nearest neighbor algorithm, Conceptual clustering, Multispectral pattern recognition, Group method of data handling, Random forest, Statistical classification, Analogical modeling, Alternating decision tree, Large margin nearest neighbor, BrownBoost, AdaBoost, Multilayer perceptron, Feature Selection Toolbox, Co-training, Variable kernel density estimation, Calibration, ID3 algorithm, C4.5 algorithm, String kernel, IDistance, CHAID, Shogun, Information gain in decision trees, Optimal discriminant analysis, AODE, Quadratic classifier, Information Fuzzy Networks, Kernel methods, Syntactic pattern recognition, Soft independent modelling of class analogies, Random subspace method, Winnow, Multiclass classification, Random multinomial logit, Class membership probabilities, Compositional pattern-producing network, Information gain ratio, ALOPEX, Relevance vector machine, Decision boundary, Features, Multiple discriminant analysis, LogitBoost, Evolving classification function, Cascading classifiers, Whitening transformation, Sukhotins Algorithm, CoBoosting, Elastic Matching, Pachinko machine.
