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/354741/10329415
m contents.m
% Support Vector Machine Toolbox
% Version 2.0-Aug-1998
%
% Support Vector Classification
%
% svc - Calculate support vectors for classification
% svcplot - Plot 2 dimensional clas
www.eeworm.com/read/354741/10329592
m contents.m
% Support Vector Machine Toolbox
% Version 2.0-Aug-1998
%
% Support Vector Classification
%
% svc - Calculate support vectors for classification
% svcplot - Plot 2 dimensional clas
www.eeworm.com/read/161538/10398343
m contents.m
% MATLAB Interface for LIBSVM, Version 1.2
%
% LIBSVMOPT Optimize Support Vector Machine with LIBSVM.
% LIBSVMSIM Simulate Support Vector Machine.
% MEXLIBSVM Compile LIBSVMOPT and LIBSVMSIM.
www.eeworm.com/read/161538/10398387
m libsvmsim.m
% LIBSVMSIM Simulate Support Vector Machine.
%
% Syntax:
% y = libsvmsim(svm,x);
% [y,p] = libsvmsim(svm,x);
%
% Input Arguments:
% svm - Support vector machine (struct, described in LIBSVMOPT
www.eeworm.com/read/161460/10407486
java catonetestresult.java
package shared;
import java.lang.*;
/** This object contains the result information on one instance passed through
* an inducer.
* @author James Louis 12/08/2000 Ported to Java.
*/
public
www.eeworm.com/read/353714/10428090
m contents.m
% Support Vector Machine Toolbox - Steve Gunn
% Version 2.0-Aug-1998
%
% Support Vector Classification
%
% svc - Calculate support vectors for classification
% svcplot - Plot 2 dim
www.eeworm.com/read/160517/10522534
m getcost.m
%GETCOST Get classification cost matrix
%
% [COST,LABLIST] = GETCOST(W)
%
% Returns the classification cost matrix as set in the classifier W.
% An empty cost matrix is interpreted as equal costs for
www.eeworm.com/read/160516/10522882
m getcost.m
%GETCOST Get classification cost matrix
%
% [COST,LABLIST] = GETCOST(A)
%
% Returns the classification cost matrix as defined for the dataset A.
% An empty cost matrix is interpreted as equal costs f
www.eeworm.com/read/277989/10587623
readme
Libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
www.eeworm.com/read/159921/10587872
m bhattach.m
function [eps]=bhattach(M1,M2,C1,C2,P1,P2)
% BHATTACH upper estimate on Bayes class. error.
% [eps]=bhattach(M1,M2,C1,C2,P1,P2)
%
% BHATTACH calculates Bhattacharya's limit, i.e. upper estimate