代码搜索：Classifier

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m ffnc.m

%FFNC Feed-forward neural net classifier back-end % % [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID) % % INPUT % ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc' %
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www.eeworm.com/read/150905/12250459

m knnfwd.m

function [y, l] = knnfwd(net, x) %KNNFWD Forward propagation through a K-nearest-neighbour classifier. % % Description % [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector % per ro
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www.eeworm.com/read/149739/12352639

m ffnc.m

%FFNC Feed-forward neural net classifier back-end % % [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID) % % INPUT % ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc' %
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www.eeworm.com/read/223154/14651897

m gdbc.m

function [GDBC,kap,acc,H,MDBC]=gdbc(ECM,Y,CL) % GDBC General discriminant-based classifier % [MDBC] = gdbc(ECM); % GDBC is a multiple discriminator % % [GDBC,kap,acc,H] = gdbc(ECM,D,CL); % calcu
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www.eeworm.com/read/220289/14843820

m knnfwd.m

function [y, l] = knnfwd(net, x) %KNNFWD Forward propagation through a K-nearest-neighbour classifier. % % Description % [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector % per ro
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www.eeworm.com/read/218623/14911998

m mil_train_validate.m

function run = MIL_train_validate(data_file, classifier) global preprocess; clear run; % The statistics of dataset % [X, Y, num_data, num_feature] = Preprocessing(D); % num_class = length(pre
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www.eeworm.com/read/218623/14912114

m mil_test_validate.m

function run = MIL_test_validate(data_file, classifier) global preprocess; clear run; % The statistics of dataset %[X, Y, num_data, num_feature] = Preprocessing(D); %num_class = length(prepro
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www.eeworm.com/read/212307/15160128

m knnfwd.m

function [y, l] = knnfwd(net, x) %KNNFWD Forward propagation through a K-nearest-neighbour classifier. % % Description % [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector % per ro
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www.eeworm.com/read/13911/286767

m svm.m

function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize) % SVM - Create a Support Vector Machine classifier % % NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE) %
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www.eeworm.com/read/482538/1286824

hh click-fastclassifier.hh

#ifndef CLICK_FASTCLASSIFIER_HH #define CLICK_FASTCLASSIFIER_HH #include #include class ElementClassT; struct Classifier_Insn { int yes; int no; int offset;
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