代码搜索：classification

找到约 3,679 项符合「classification」的源代码

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asv code.asv

function [nsignals, codebook, oldcodebook, scheme] = code(signals,codetype,codetype_args,oldcodebook,fctdist,fctdist_args) % Encode and decode a multi-class classification task into multiple binary cl
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www.eeworm.com/read/175683/9536331

m demsvm2.m

function demsvm2() % DEMSVM2 - Demonstrate advanced Support Vector Machine features % % DEMSVM2 demonstrates the classification of a simple artificial data % set by a Support Vector Machine clas
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www.eeworm.com/read/175683/9536373

asv demsvm2.asv

function demsvm2() % DEMSVM2 - Demonstrate advanced Support Vector Machine features % % DEMSVM2 demonstrates the classification of a simple artificial data % set by a Support Vector Machine clas
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www.eeworm.com/read/362199/10013176

asv knn.asv

function [eachClass, nearestSampleIndex, knnmat] = ... knn(sampledata, testdata, k) % KNN K-nearest neighbor rule for classification % Usage: % [EACH_CLASS, NEAREST_SAMPLE_INDEX] = KNN(SAMPLE, INPUT,
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www.eeworm.com/read/362199/10013178

m knn.m

function [eachClass, nearestSampleIndex, knnmat] = ... knn(sampledata, testdata, k) % KNN K-nearest neighbor rule for classification % Usage: % [EACH_CLASS, NEAREST_SAMPLE_INDEX] = KNN(SAMPLE, INPUT,
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www.eeworm.com/read/425546/10349078

m demev2.m

%DEMEV2 Demonstrate Bayesian classification for the MLP. % % Description % A synthetic two class two-dimensional dataset X is sampled from a % mixture of four Gaussians. Each class is associated
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www.eeworm.com/read/353335/10453952

m hddc_classif.m

function [cls,P] = hdda_classif2_faster(prms,Y,varargin); % High Dimensionality Discriminant Analysis (classification) % % Usage: (1) [cls,P] = hdda_classif(prms,Y); % % Input: % -
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www.eeworm.com/read/353334/10453962

m hdda_classif.m

function [cls,P] = hdda_classif2_faster(prms,Y,varargin); % High Dimensionality Discriminant Analysis (classification) % % Usage: (1) [cls,P] = hdda_classif(prms,Y); % % Input: % -
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www.eeworm.com/read/278889/10490587

m code.m

function [nsignals, codebook, oldcodebook, scheme] = code(signals,codetype,codetype_args,oldcodebook,fctdist,fctdist_args) % Encode and decode a multi-class classification task into multiple binary cl
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www.eeworm.com/read/424119/10490904

c srng_model.c

/* Supervised Relevance Natural Gaz classification algorithm. Usage ------ [Wproto_est , yproto_est , E_SRNG] = srng_model(Xtrain , ytrain , [Wproto] , [yproto] , [lambda] , [optio
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