代码搜索：classifiers
找到约 2,305 项符合「classifiers」的源代码
代码结果 2,305
www.eeworm.com/read/400577/11573018
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/400577/11573362
m fixedcc.m
%FIXEDCC Construction of fixed combiners
%
% V = FIXEDCC(A,W,TYPE,NAME)
%
% INPUT
% A Dataset
% W A set of classifier mappings
% TYPE The type of combination rule
% NAME The na
www.eeworm.com/read/400577/11573374
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/346712/11729555
java checkgoe.java
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either vers
www.eeworm.com/read/342008/12046762
m medianc.m
%MEDIANC Median combining classifier
%
% W = medianc(V)
% W = V*medianc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the median combiner: it selects
www.eeworm.com/read/342008/12046820
m minc.m
%MINC Minimum combining classifier
%
% W = minc(V)
% W = V*minc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the minimum combiner: it selects the cla
www.eeworm.com/read/342008/12046941
m prodc.m
%PRODC Product combining classifier
%
% W = prodc(V)
% W = V*prodc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the product combiner: it selects the
www.eeworm.com/read/342008/12047259
m meanc.m
%MEANC Averaging combining classifier
%
% W = meanc(V)
% W = V*meanc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the mean combiner: it selects the c
www.eeworm.com/read/255755/12057275
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/255755/12057414
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