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/255755/12057948
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/255755/12058314
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/150905/12248294
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/150905/12249244
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/150905/12249700
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/150749/12267363
m code.m
function [nsignals, codebook, oldcodebook, scheme] = code(signals,codetype,codetype_args,oldcodebook,fctdist,fctdist_args)
% Encode and decode a multi-class classification task into multiple binary cl
www.eeworm.com/read/149739/12352668
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/149739/12353543
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/149739/12353959
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/128193/14311428
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