代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

代码结果 4,824
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m bayescln.m

function [I,Pkx]=bayescln(X,MI,SIGMA,Pk) % BAYESCLN Bayes classifier for Gaussian distributiuon. % [I,Pkx]=bayescln(X,MI,SIGMA,Pk) % % This function classifies into the class according to the %
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www.eeworm.com/read/430506/1929410

m~ bayescln.m~

function [I,Pkx]=bayescln(X,MI,SIGMA,Pk) % BAYESCLN Bayes classifier for Gaussian distributiuon. % [I,Pkx]=bayescln(X,MI,SIGMA,Pk) % % This function classifies into the class according to the %
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www.eeworm.com/read/428780/1954024

m~ rspoly2.m~

function red_model = redquadh(model) % REDQUADH reduced SVM classifier with homogeneous quadratic kernel. % % Synopsis: % red_model = redquadh(model) % % Description: % It uses reduced set techique
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www.eeworm.com/read/428780/1954043

m redquadh.m

function red_model = redquadh(model) % REDQUADH reduced SVM classifier with homogeneous quadratic kernel. % % Synopsis: % red_model = redquadh(model) % % Description: % It uses reduced set techique
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www.eeworm.com/read/428780/1954206

m~ rspoly2.m~

function red_model = redquadh(model) % REDQUADH reduced SVM classifier with homogeneous quadratic kernel. % % Synopsis: % red_model = redquadh(model) % % Description: % It uses reduced set techique
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www.eeworm.com/read/428780/1954236

m redquadh.m

function red_model = redquadh(model) % REDQUADH reduced SVM classifier with homogeneous quadratic kernel. % % Synopsis: % red_model = redquadh(model) % % Description: % It uses reduced set techique
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www.eeworm.com/read/428780/1954250

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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www.eeworm.com/read/428780/1954260

asv linclass.asv

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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www.eeworm.com/read/411379/2188960

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a support vector classifier network using the specified tutor. % % load data/iris x y; % % C = 100; % kernel = r
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www.eeworm.com/read/396844/2406659

m demknn1.m

%DEMKNN1 Demonstrate nearest neighbour classifier. % % Description % The problem consists of data in a two-dimensional space. The data is % drawn from three spherical Gaussian distributions with prio
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