代码搜索:classification

找到约 3,679 项符合「classification」的源代码

代码结果 3,679
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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 %
www.eeworm.com/read/181389/9256463

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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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 %
www.eeworm.com/read/181388/9256596

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 %
www.eeworm.com/read/181388/9256653

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 %
www.eeworm.com/read/177674/9442655

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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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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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
www.eeworm.com/read/362500/9995992

m imgsimca.m

function model = imgsimca(img,data) %IMGSIMCA SIMCA Classification for Multivariate Images % This function allows the user to build SIMCA class models % by outlining areas on a reference image (img)
www.eeworm.com/read/357125/10215873

java prediction.java

package mulan.classifier; import weka.core.Utils; /** * Simple container class for multilabel classification result */ public class Prediction { protected double[] confidences; pro