代码搜索：classifiers
找到约 2,305 项符合「classifiers」的源代码
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www.eeworm.com/read/418756/10928173
m adademo.m
function MOV=adademo
% ADADEMO AdaBoost demo
% ADADEMO runs AdaBoost on a simple two dimensional classification
% problem.
% Written by Andrea Vedaldi - 2006
% http://vision.ucla.edu/~vedaldi
do_
www.eeworm.com/read/418755/10928192
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/418695/10935160
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/418695/10935183
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/418695/10935239
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/418695/10935438
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/467949/6997143
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/299984/7139979
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/299984/7140038
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/299984/7140054
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