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 maxc.m

%MAXC Maximum combining classifier % % W = maxc(V) % W = V*maxc % % If V = [V1,V2,V3, ... ] is a set of classifiers trained on the % same classes and W is the maximum combiner: it selects the cla
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www.eeworm.com/read/431675/8662312

m fisherc.m

%FISHERC Fisher's Least Square Linear Classifier % % W = fisherc(A,mode,n) % % Finds the linear discriminant function between the classes in the % dataset A by minimizing the errors in the least s
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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/386050/8767456

m knn_map.m

%KNN_MAP Map a dataset on a K-NN classifier % % F = KNN_MAP(A,W) % % INPUT % A Dataset % W K-NN classifier trained by KNNC % % OUTPUT % F Posterior probabilities % % DESCRIPTION % Maps t
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www.eeworm.com/read/386050/8767493

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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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/386050/8767558

m contents.m

% Pattern Recognition Tools % Version 4.1.4 11-Oct-2008 % %Datasets and Mappings (just most important routines) %--------------------- %dataset Define dataset from datamatrix and labels %datasets
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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/386050/8768946

m klldc.m

%KLLDC Linear classifier built on the KL expansion of the common covariance matrix % % W = KLLDC(A,N) % W = KLLDC(A,ALF) % % INPUT % A Dataset % N Number of significant eigenvectors % AL
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www.eeworm.com/read/386050/8768981

m tree_map.m

%TREE_MAP Map a dataset by binary decision tree % % F = TREE_MAP(A,W) % % INPUT % A Dataset % W Decision tree mapping % % OUTPUT % F Posterior probabilities % % DESCRIPTION % Maps the dataset
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