代码搜索：classification

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m genetic_programming.m

function [D, best_fun] = genetic_programming(features, targets, params, region) % A genetic programming algorithm for classification % % features - Train features % targets - Train targets
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www.eeworm.com/read/413912/11137378

m demtrain.m

function demtrain(action); %DEMTRAIN Demonstrate training of MLP network. % % Description % DEMTRAIN brings up a simple GUI to show the training of an MLP % network on classification and regression pr
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www.eeworm.com/read/112466/15484864

txt readme.txt

=== GSNAKE API ver 1.0 === Introduction ============ GSNAKE API provides tools for contour modeling, extraction, detection and classification, based on generalized active contour model (g-snak
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www.eeworm.com/read/111603/15509322

m getbias.m

function bias = getbias(net) % GETBIAS % % Accessor method returning the bias of a support vector classification % network. % % bias = getbias(net); % % File : @svc/getbias.m %
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www.eeworm.com/read/111603/15509353

m svctutor.m

function tutor = svctutor(arg) % SVCTUTOR % % Constructor for a class of tutor objects used to train support vector % classification networks. Note this is an abstract base class, you cannot %
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www.eeworm.com/read/289680/8534967

m svc.m

function net = svc(arg, sv, w, bias) % SVC % % Construct a support vector classification (SVC) network object. % % Examples: % % % default constructor (linear, hardmargin SVC with no suppo
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www.eeworm.com/read/289680/8535153

m pairwise.m

function net = pairwise(arg) % PAIRWISE % % Construct a pairwise multi-class support vector classification network. % % Examples: % % % default constructor (a 0-class pairwise network!) %
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www.eeworm.com/read/188280/8552099

m svc.m

function net = svc(arg, sv, w, bias) % SVC % % Construct a support vector classification (SVC) network object. % % Examples: % % % default constructor (linear, hardmargin SVC with no suppo
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www.eeworm.com/read/188280/8552297

m pairwise.m

function net = pairwise(arg) % PAIRWISE % % Construct a pairwise multi-class support vector classification network. % % Examples: % % % default constructor (a 0-class pairwise network!) %
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www.eeworm.com/read/431675/8661711

m gendats.m

%GENDATS Generation of a simple classification problem % % A = gendats(na,nb,k,d) % % Generation of a two class k dimensional dataset A. Both classes % are Gaussian distributed with identy matrix
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