代码搜索：classification

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m contents.m

% Support Vector Machine Toolbox % Version 2.0-Aug-1998 % % Support Vector Classification % % svc - Calculate support vectors for classification % svcplot - Plot 2 dimensional clas
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m contents.m

% Support Vector Machine Toolbox % Version 2.0-Aug-1998 % % Support Vector Classification % % svc - Calculate support vectors for classification % svcplot - Plot 2 dimensional clas
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m classd.m

%CLASSD Classify data using a given classifier % % labels = classd(D) % % Finds the labels of the classified dataset D (typically the result % of a mapping or classification A*W). For each object
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html readme.html

Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
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m labeld.m

%LABELD Find labels of classification dataset (perform crisp classification) % % LABELS = LABELD(Z) % LABELS = Z*LABELD % LABELS = LABELD(A,W) % LABELS = A*W*LABELD % LABELS = LABELD(Z,THRE
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m getcost.m

%GETCOST Get classification cost matrix % % [COST,LABLIST] = GETCOST(A) % % Returns the classification cost matrix as defined for the dataset A. % An empty cost matrix is interpreted as equal costs f
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m getcost.m

%GETCOST Get classification cost matrix % % [COST,LABLIST] = GETCOST(W) % % Returns the classification cost matrix as set in the classifier W. % An empty cost matrix is interpreted as equal costs for
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htm demtrain.htm

Netlab Reference Manual demtrain demtrain Purpose Demonstrate training of MLP network. Synopsis demtrain
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readme

Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
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m contents.m

% Bayesian classification. % % bayescls - Bayesian classifier with reject option. % bayesdf - Computes decision boundary of Bayesian classifier. % bayeserr - Computes Bayesian risk for 1D case with G
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