代码搜索：Classifier

找到约 4,824 项符合「Classifier」的源代码

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www.eeworm.com/read/137160/13341864

m rbnc.m

%RBNC Radial basis function neural network classifier % % W = RBNC(A,UNITS) % % INPUT % A Dataset % UNITS Number of RBF units in hidden layer % % OUTPUT % W Radial basis neural n
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www.eeworm.com/read/137160/13342337

m costm.m

%COSTM Cost mapping, classification using costs % % Y = COSTM(X,C,LABLIST) % W = COSTM([],C,LABLIST) % % DESCRIPTION % Maps the classifier output X (assumed to be posterior probability % estimate
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www.eeworm.com/read/314653/13562195

m spatm.m

%SPATM Augment image dataset with spatial label information % % E = SPATM(D,S) % E = D*SPATM([],S) % % INPUT % D image dataset classified by a classifier % S smoothing paramet
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www.eeworm.com/read/314653/13562234

m averagec.m

%AVERAGEC Combining of linear classifiers by averaging coefficients % % W = AVERAGEC(V) % W = V*AVERAGEC % % INPUT % V A set of affine base classifiers. % % OUTPUT % W Combined classifier. % %
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www.eeworm.com/read/314653/13562236

m rbnc.m

%RBNC Radial basis function neural network classifier % % W = RBNC(A,UNITS) % % INPUT % A Dataset % UNITS Number of RBF units in hidden layer % % OUTPUT % W Radial basis neural n
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www.eeworm.com/read/314653/13562555

m costm.m

%COSTM Cost mapping, classification using costs % % Y = COSTM(X,C,LABLIST) % W = COSTM([],C,LABLIST) % % DESCRIPTION % Maps the classifier output X (assumed to be posterior probability % estimate
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www.eeworm.com/read/312163/13617411

m psvm.m

function varargout=psvm(model,options) % PSVM Plots decision boundary of binary SVM classifier. % % Synopsis: % h = psvm(...) % psvm(model) % psvm(model,options) % % Description: % This function s
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www.eeworm.com/read/312163/13617561

m perceptron.m

function model=perceptron(data,options,init_model) % PERCEPTRON Perceptron algorithm to train binary linear classifier. % % Synopsis: % model = perceptron(data) % model = perceptron(data,options) %
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www.eeworm.com/read/134901/5891550

m perceptron.m

function model=perceptron(data,options,init_model) % PERCEPTRON Perceptron algorithm to train binary linear classifier. % % Synopsis: % model = perceptron(data) % model = perceptron(data,options) %
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www.eeworm.com/read/128684/5980322

m getsv.m

function sv = getsv(net) % GETSV % % Accessor method returning the support vectors of a support vector % classifier network. % % sv = getsv(net); % % File : @svc/getsv.m % % D
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