代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/450608/7480573
m featsellr.m
%FEATSELLR Plus-L-takeaway-R feature selection for classification
%
% [W,RES] = FEATSELLR(A,CRIT,K,L,R,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
www.eeworm.com/read/441245/7672635
m gendats.m
%GENDATS Generation of a simple classification problem of 2 Gaussian classes
%
% A = GENDATS (N,K,D,LABTYPE)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]
www.eeworm.com/read/441245/7673035
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K
www.eeworm.com/read/441245/7673051
m featself.m
%FEATSELF Forward feature selection for classification
%
% [W,R] = FEATSELF(A,CRIT,K,T,FID)
% [W,R] = FEATSELF(A,CRIT,K,N,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or u
www.eeworm.com/read/441245/7673253
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%FDSC Feature based Dissimilarity Space Classification (outdated)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% This routine is outdated, use KERNELC instead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/441245/7673397
m featselb.m
%FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
% [W,R] = FEATSELB(A,CRIT,K,N,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion o
www.eeworm.com/read/441245/7673400
m featsellr.m
%FEATSELLR Plus-L-takeaway-R feature selection for classification
%
% [W,RES] = FEATSELLR(A,CRIT,K,L,R,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
www.eeworm.com/read/398324/7994141
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each column represent
www.eeworm.com/read/398324/7994255
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each column represent
www.eeworm.com/read/397758/8024492
m mixclass.m
function [clabs, err] = mixclass(data,pies,mus,vars)
% MIXCLASS Get the classification from a mixture model.
%
% [CLABS,ERR] = MIXCLASS(DATA,WGTS,MUS,VARS)
%
% For a given set of DATA (nxd