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/351797/10609677
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/421949/10676133
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/421949/10676308
asv code.asv
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/270943/11019013
_readme_
Some directions and helpful hints on how to work with the region extraction/
shape classification system.
NEW FEATURES:
- region comparison now gives additional info, i.e. ratios of areas,
volum
www.eeworm.com/read/469416/6976404
m demev2.m
%DEMEV2 Demonstrate Bayesian classification for the MLP.
%
% Description
% A synthetic two class two-dimensional dataset X is sampled from a
% mixture of four Gaussians. Each class is associated
www.eeworm.com/read/299984/7139956
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/299984/7140346
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/299984/7140362
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/299984/7140559
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%FDSC Feature based Dissimilarity Space Classification (outdated)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% This routine is outdated, use KERNELC instead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/299984/7140701
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