代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

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m~ contents.m~

% Support Vector Machines. % % bsvm2 - Multi-class BSVM with L2-soft margin. % evalsvm - Trains and evaluates Support Vector Machines classifier. % mvsvmclass - Majority voting multi-cla
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m weaklearner.m

function model = weaklearner(data) % WEAKLEARNER Produce classifier thresholding single feature. % % Synopsis: % model = weaklearner(data) % % Description: % This function produce a weak binary clas
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m cerror.m

function error=cerror(y1,y2,label) % CERROR Computes classification error. % % Synopsis: % error = cerror(y1,y2) % error = cerror(y1,y2,label) % % Description: % error = cerror(y1,y2) returns clas
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m linclass.m

function [y,dfce]=linclass( X, model) % LINCLASS Linear classifier. % % Synopsis: % [y,dfce] = linclass( X, model) % % Description: % This function classifies input data X using linear % discrimina
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m osusvmdemo.m

% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations--- % % 1) Demonstrations of using C-SVM Classifers. % 2) Demonstrations of using u-SVM Classifiers % 3) Demonstration
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www.eeworm.com/read/398324/7994453

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a dag-svm multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a dag-svm multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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m udc.m

%UDC Uncorrelated normal based quadratic Bayes classifier % % W = udc(A) % % Computation a quadratic classifier between the classes in the % dataset A assuming normal densities with uncorrelated f
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www.eeworm.com/read/397102/8068534

m traincc.m

%TRAINCC Train combining classifier if needed % % W = traincc(A,W,cclassf) % % The combining classifier cclassf is trained by dataset A*W if it needs % training. W is typically a set of stacked or par
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www.eeworm.com/read/140853/13058119

m osusvmdemo.m

% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations--- % % 1) Demonstrations of using C-SVM Classifers. % 2) Demonstrations of using u-SVM Classifiers % 3) Demonstration
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