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/431675/8661745
m parzen_map.m
%PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = parzen_map(A,W)
%
% Maps the dataset A by the Parzen density based classfier W. F*sigm
% are the posterior probabilities. W
www.eeworm.com/read/431675/8661807
m nmc.m
%NMC Nearest Mean Classifier
%
% W = nmc(A)
%
% Computation of the nearest mean classifier between the classes in
% the dataset A.
%
% See also datasets, mappings, nmsc, ldc, fisherc, qdc, udc
www.eeworm.com/read/431675/8662173
m prex4.m
%PREX4 PRTOOLS example of classifier combining
help prex4
echo on
A = gendatd(100,100,10);
[B,C] = gendat(A,20);
wkl = klm(B,0.95); % find KL mapping input space
bkl = B*wkl; % map training
www.eeworm.com/read/431306/8689547
cpp scs.cpp
/****************************************************************************/
/* 基本遗传学习分类系统 SCS.CPP */
/* A Simple Classifier System based on G
www.eeworm.com/read/386050/8767430
m knnc.m
%KNNC K-Nearest Neighbor Classifier
%
% [W,K,E] = KNNC(A,K)
% [W,K,E] = KNNC(A)
%
% INPUT
% A Dataset
% K Number of the nearest neighbors (optional; default: K is
% optimized with resp
www.eeworm.com/read/386050/8767546
m pcldc.m
%PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (defau
www.eeworm.com/read/386050/8768084
m kernelc.m
%KERNELC Arbitrary kernel/dissimilarity based classifier
%
% W = KERNELC(A,KERNEL,CLASSF)
% W = A*KERNELC([],KERNEL,CLASSF)
%
% INPUT
% A Dateset used for training
% KERNEL - unt
www.eeworm.com/read/386050/8769028
m fdsc.m
%FDSC Feature based Dissimilarity Space Classification (outdated)
%
% This routine is outdated, use KERNELC instead
%
% W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
% W = A*FDSC([],R,FEATMAP,TYPE,P,C
www.eeworm.com/read/386050/8769413
m mclassc.m
%MCLASSC Computation of multi-class classifier from 2-class discriminants
%
% W = MCLASSC(A,CLASSF,MODE)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier
% MODE Type of handling mu
www.eeworm.com/read/386050/8769683
m rsscc.m
%RSSCC Random subspace combining classifier
%
% W = RSSCC(A,CLASSF,NFEAT,NCLASSF)
%
% INPUT
% A Dataset
% CLASSF Untrained base classifier
% NFEAT Number of features for train