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/314653/13562700
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/314653/13562710
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/493294/6399927
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/493294/6400014
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/493294/6400484
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/493294/6400496
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/489934/6463612
txt readme.txt
README
--------
Directory contains the following files.
1. ADABOOST_te.m
2. ADABOOST_tr.m
3. demo.m
4. likelihood2class.m
5. threshold_te.m
6. threshold_tr.m
The aim of the project is to provide
www.eeworm.com/read/400577/11572623
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/400577/11572685
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/400577/11572703
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