代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

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m getcost.m

%GETCOST Get classification cost matrix % % [COST,LABLIST] = GETCOST(W) % % Returns the classification cost matrix as set in the classifier W. % An empty cost matrix is interpreted as equal costs for
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www.eeworm.com/read/400576/11573550

m incsvdd.m

%INCSVDD Incremental Support Vector Classifier % % W = INCSVDD(A,FRACERR,KTYPE,PAR) % % Use the incremental version of the SVDD. The kernel is defined by % KTYPE, with the free parameter PAR. See
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www.eeworm.com/read/342008/12046754

m invsigm.m

%INVSIGM Inverse sigmoid map % % W = W*invsigm % B = invsigm(A) % % Inverse sigmoidal transformation from classifier to map, transforming % posterior probabilities into distances. % % See also da
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www.eeworm.com/read/342008/12047271

m cleval.m

%CLEVAL Classifier evaluation (learning curve) % % [e,s] = cleval(classf,A,learnsizes,n,T,print) % % Generates at random for all class sizes of the training set % defined in the vector 'learnsizes
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www.eeworm.com/read/342008/12047273

m clevalb.m

%CLEVAL Classifier evaluation (learning curve), bootstrap version % % [e,s] = cleval(classf,A,learnsizes,n,T,print) % % Generates at random for all class sizes of the training set % defined in the
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www.eeworm.com/read/342008/12047274

m subsc.m

%SUBSC Subspace Classifier % % W = subsc(A,n) % % n-dimensional subspace maps are computed for each class of the dataset A % using PCA, such that they contain the origin. All object in A are normalize
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www.eeworm.com/read/255755/12057883

m nbayesc.m

%NBAYESC Bayes Classifier for given normal densities % % W = NBAYESC(U,G) % % INPUT % U Dataset of means of classes % G Covariance matrices (optional; default: identity matrices) % % OUTP
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www.eeworm.com/read/255755/12057974

m neurc.m

%NEURC Automatic neural network classifier % % W = NEURC (A,UNITS) % % INPUT % A Dataset % UNITS Array indicating number of units in each hidden layer (default: [5]) % % OUTPUT % W Tra
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www.eeworm.com/read/255755/12057976

m testp.m

%TESTP Error estimation of Parzen classifier % % E = TESTP(A,H,T) % E = TESTP(A,H) % % INPUT % A input dataset % H matrix smoothing parameters (optional, def: determined via %
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www.eeworm.com/read/255755/12058005

m bayesc.m

%BAYESC Bayes classifier % % W = BAYESC(WA,WB, ... ,P,LABLIST) % % INPUT % WA, WB, ... Trained mappings for supplying class density estimates % P Vector with class prior probabili
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