代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/460435/7250450
m loglc.m
%LOGLC Logistic Linear Classifier
%
% W = LOGLC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Logistic linear classifier
%
% DESCRIPTION
% Computation of the linear classifier for the dataset
www.eeworm.com/read/460435/7250454
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/460435/7250476
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/460435/7250513
m weakc.m
%WEAKC Weak Classifier
%
% [W,V] = WEAKC(A,ALF,ITER,R)
% VC = WEAKC(A,ALF,ITER,R,1)
%
% INPUT
% A Dataset
% ALF Fraction of objects to be used for training (def: 0.5)
% ITER Numb
www.eeworm.com/read/460435/7250526
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/460435/7250529
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/460435/7250791
m rejectc.m
%REJECTC Construction of a rejecting classifier
%
% WR = REJECTC(A,W,FRAC,TYPE)
%
% INPUT
% A Dataset
% W Trained or untrained classifier
% FRAC Fraction to be rejected. Def
www.eeworm.com/read/460435/7250828
m nusvc.m
%NUSVC Support Vector Classifier: NU algorithm
%
% [W,J] = NUSVC(A,KERNEL,NU)
% [W,J] = NUSVC(A,TYPE,PAR,NU)
% W = A*SVC([],KERNEL,NU)
% W = A*SVC([],TYPE,PAR,NU)
%
% INPUT
% A
www.eeworm.com/read/460435/7250840
m wvotec.m
%WVOTEC Weighted combiner (Adaboost weights)
%
% W = WVOTEC(A,V) compute weigths and store
% W = WVOTEC(V,U) Construct weighted combiner using weights U
%
% INPUT
% A Labeled data
www.eeworm.com/read/460435/7251189
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