代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

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m roc.m

function [AREA,SE,RESULT_S,FPR_ROC,TPR_ROC,TNa,TPa,FNa,FPa]=roc(RESULT,CLASS,fig) % Receiver Operating Characteristic (ROC) curve of a binary classifier % % >> [area, se, deltab, oneMinusSpec, sen
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www.eeworm.com/read/324303/13273802

m roc.m

function [AREA,SE,RESULT_S,FPR_ROC,TPR_ROC,TNa,TPa,FNa,FPa]=roc(RESULT,CLASS,fig) % Receiver Operating Characteristic (ROC) curve of a binary classifier % % >> [area, se, deltab, oneMinusSpec, sen
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www.eeworm.com/read/137160/13341790

m medianc.m

%MEDIANC Median combining classifier % % W = MEDIANC(V) % W = V*MEDIANC % % INPUT % V Set of classifiers % % OUTPUT % W Median combining classifier on V % % DESCRIPTION % If V = [V
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www.eeworm.com/read/137160/13341894

m classc.m

%CLASSC Convert mapping to classifier % % W = CLASSC(W) % W = W*CLASSC % % INPUT % W Any mapping or dataset % % OUTPUT % W Classifier mapping or normalized dataset: outputs/features sum to 1 %
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www.eeworm.com/read/137160/13341917

m prodc.m

%PRODC Product combining classifier % % W = PRODC(V) % W = V*PRODC % % INPUT % V Set of classifiers trained on the same classes % % OUTPUT % W Product combiner % % DESCRIPTION % It def
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www.eeworm.com/read/137160/13341925

m contents.m

% Pattern Recognition Tools % Version 4.0.14 04-Mar-2005 % %Datasets and Mappings (just most important routines) %--------------------- %dataset Define and retrieve dataset from datamatrix and lab
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www.eeworm.com/read/137160/13342037

m contents.m

% Pattern Recognition Tools % Version URV 24-Mar-2004 % % This is prelimanary, many support routines in ./private ./@datasets % and ./@mappings are not mentioned. % %Datasets and Mappings (just most i
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www.eeworm.com/read/137160/13342189

m prtools.m

% Pattern Recognition Tools % Version URV 24-Mar-2004 % % This is prelimanary, many support routines in ./private ./@datasets % and ./@mappings are not mentioned. % %Datasets and Mappings (just most i
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www.eeworm.com/read/137160/13342273

m meanc.m

%MEANC Mean combining classifier % % W = MEANC(V) % W = V*MEANC % % INPUT % V Set of classifiers (optional) % % OUTPUT % W Mean combiner % % DESCRIPTION % If V = [V1,V2,V3, ... ] is a s
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www.eeworm.com/read/137160/13342310

m ldc.m

%LDC Linear Bayes Normal Classifier (BayesNormal_1) % % W = LDC(A,R,S) % % INPUT % A Dataset % R,S Regularization parameters, 0
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