代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/137160/13341944
m parsc.m
%PARSC Parse classifier
%
% PARSC(W)
%
% Displays the type and, for combining classifiers, the structure of the
% mapping W.
%
% See also MAPPINGS
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
www.eeworm.com/read/137160/13341951
m parallel.m
%PARALLEL Combining classifiers in different feature spaces
%
% WC = PARALLEL(W1,W2,W3, ....) or WC = [W1;W2;W3; ...]
% WC = PARALLEL({W1;W2;W3; ...}) or WC = [{W1;W2;W3; ...}]
% WC = PARALL
www.eeworm.com/read/137160/13342252
m nmsc.m
%NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the linear discrim
www.eeworm.com/read/137160/13342622
m traincc.m
%TRAINCC Train combining classifier if needed
%
% W = TRAINCC(A,W,CCLASSF)
%
% INPUT
% A Training dataset
% W A set of classifiers to be combined
% CCLASSF Combining classif
www.eeworm.com/read/137160/13342697
m testcost.m
function e = testcost(x,w,C,lablist)
%TESTCOST compute the error using the cost matrix C
%
% E = TESTCOST(A,W,C,LABLIST)
% E = TESTCOST(A*W,C,LABLIST)
% E = A*W*TESTCOST([],C,LABLIST)
%
%
www.eeworm.com/read/314653/13562230
m baggingc.m
%BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = BAGGINGC (A,CLASSF,N,ACLASSF,T)
%
% INPUT
% A Training dataset.
% CLASSF The base classifier (default: nmc)
% N
www.eeworm.com/read/314653/13562247
m polyc.m
%POLYC Polynomial Classification
%
% W = polyc(A,CLASSF,N,S)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier (optional; default: FISHERC)
% N Degree of polynomial (optional;
www.eeworm.com/read/314653/13562282
m parsc.m
%PARSC Parse classifier
%
% PARSC(W)
%
% Displays the type and, for combining classifiers, the structure of the
% mapping W.
%
% See also MAPPINGS
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
www.eeworm.com/read/314653/13562285
m parallel.m
%PARALLEL Combining classifiers in different feature spaces
%
% WC = PARALLEL(W1,W2,W3, ....) or WC = [W1;W2;W3; ...]
% WC = PARALLEL({W1;W2;W3; ...}) or WC = [{W1;W2;W3; ...}]
% WC = PARALL
www.eeworm.com/read/314653/13562510
m nmsc.m
%NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the linear discrim