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/342008/12047351
m getclass.m
%GETCLASS Get classifier bit of mapping
function classbit = getclass(w)
classbit = w.s;
return
www.eeworm.com/read/342008/12047403
m isclassifier.m
%ISCLASSIFIER Get classifier bit of mapping
function classbit = isclassifier(w)
classbit = w.s;
return
www.eeworm.com/read/342008/12047428
m mclassc.m
%MCLASSC Computation of multi-class classifier from 2-class discriminants
%
% W = mclassc(A,classf)
%
% The untrained classifier classf is called to compute c classifiers
% between each of the c class
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/255755/12057316
m knn_map.m
%KNN_MAP Map a dataset on a K-NN classifier
%
% F = KNN_MAP(A,W)
%
% INPUT
% A Dataset
% W k-NN classifier trained by KNNC
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps t
www.eeworm.com/read/255755/12057320
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/255755/12057405
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/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
www.eeworm.com/read/255755/12057879
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/255755/12058335
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