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/415313/11076766
m mcwithmultifset.m
% MCWithMultiFSet: implementation for meta-classification on seperate
% groups of features (i.e., a late fusion strategy)
%
% Parameters:
% classifier: base classifier
% para: parameters
% 1
www.eeworm.com/read/415313/11076983
m mcwithvoting.m
% MCWithVoting: implementation for meta-classification with majority voting
%
% Parameters:
% classifier: base classifier
% para: parameters
% 1. PosRatio: ratio of positive examples after s
www.eeworm.com/read/413489/11154155
m isodata.m
%**** ISODATA classification algorithm simulation ****%
% Author: Feng Shuo
% Student ID: 1030520508
% Date 2007.04.15 %special day for me.......
% 2007.04.19 updated
clc;
clear;
%% Initial
www.eeworm.com/read/413489/11154168
m isodata1.m
%**** ISODATA classification algorithm simulation ****%
% Author: Feng Shuo
% Student ID: 1030520508
% Date 2007.04.15 %special day for me.......
% 2007.04.19 updated
clc;
clear;
%% Initial
www.eeworm.com/read/413479/11154364
m isodata.m
%**** ISODATA classification algorithm simulation ****%
% Author: Feng Shuo
% Student ID: 1030520508
% Date 2007.04.15 %special day for me.......
% 2007.04.19 updated
clc;
clear;
%% Initial
www.eeworm.com/read/411674/11232980
m contents.m
% Statistical Pattern Recognition Toolbox (STPRtool).
% Version 2.04 22-Dec-2004
%
% Bayesian classification.
% bayescls - Bayesian classifier with reject option.
% bayesdf
www.eeworm.com/read/410973/11262480
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/111603/15509319
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/111603/15509321
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/111603/15509380
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 : @dags