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/312163/13617610
m~ contents.m~
% Statistical Pattern Recognition Toolbox (STPRtool).
% Version 2.07 17-Jun-2007
%
% Bayesian classification.
% bayescls - Bayesian classifier with reject option.
% bayesdf
www.eeworm.com/read/128684/5980324
m getnsv.m
function nsv = getnsv(net)
% GETNSV
%
% Accessor method returning the number of support vectors of a support vector
% classification network.
%
% n = getnsv(net);
%
% File : @svc/
www.eeworm.com/read/128684/5980326
m strip.m
function net = strip(net, tolerance)
% STRIP
%
% Delete support vectors from a support vector classification network for which
% the magnitude of the corresponding weight is less than a given to
www.eeworm.com/read/152024/6334040
m kclassify.m
function groups = kclassify(samples, means)
% KCLASSIFY Classification based on k-means (no dependence on dimension)
%
% Samples: A matrix with rows containing sample vectors
% Means: A matrix with
www.eeworm.com/read/493294/6399881
m feateval.m
%FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untraine
www.eeworm.com/read/493294/6400482
m featselb.m
%FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
% (opti
www.eeworm.com/read/483114/6609669
m getnsv.m
function nsv = getnsv(net)
% GETNSV
%
% Accessor method returning the number of support vectors of a support vector
% classification network.
%
% n = getnsv(net);
%
% File : @svc/
www.eeworm.com/read/483114/6609671
m strip.m
function net = strip(net, tolerance)
% STRIP
%
% Delete support vectors from a support vector classification network for which
% the magnitude of the corresponding weight is less than a given to
www.eeworm.com/read/409871/11309617
txt 5-1286msg1.txt
Subject: re : 5 . 1254 typological classification
for what it be worth , i disagree with martin haspelmath ( and agree with fritz newmeyer ) about the problem of define the concept with which typolog
www.eeworm.com/read/400577/11572583
m feateval.m
%FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untraine