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/128684/5980371
m pairwise.m
function net = pairwise(arg)
% PAIRWISE
%
% Construct a pairwise multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class pairwise network!)
%
www.eeworm.com/read/128293/5992035
java clientoutput.java
package ConnectAdapter;
import Classification.ClassifyConfig;
import java.io.PrintWriter;
import Kernel.CommodityInfo;
/**
* Created by IntelliJ IDEA.
* User: Administrator
* Date: 2
www.eeworm.com/read/485544/6552646
m conffig.m
function fh=conffig(y, t)
%CONFFIG Display a confusion matrix.
%
% Description
% CONFFIG(Y, T) displays the confusion matrix and classification
% performance for the predictions mat{y} compared with
www.eeworm.com/read/483114/6609664
m svc.m
function net = svc(arg, sv, w, bias)
% SVC
%
% Construct a support vector classification (SVC) network object.
%
% Examples:
%
% % default constructor (linear, hardmargin SVC with no suppo
www.eeworm.com/read/483114/6609793
m pairwise.m
function net = pairwise(arg)
% PAIRWISE
%
% Construct a pairwise multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class pairwise network!)
%
www.eeworm.com/read/482624/6620012
c lssvm_classificator.c
#include "lssvm_classificator.h"
/*
* constructor of structure containing all info for classification
*
*
*/
lssvm_c* createLSSVMClassificator(const double* svX, const int dimX,
const do
www.eeworm.com/read/157733/11667404
c prind.c
/* Weight-setting and scoring implementation for PrInd classification
(Fuhr's Probabilistic Indexing) */
/* Copyright (C) 1997 Andrew McCallum
Written by: Andrew Kachites McCallum
www.eeworm.com/read/342008/12046805
m gendats.m
%GENDATS Generation of a simple classification problem
%
% A = gendats(na,nb,k,d)
%
% Generation of a two class k dimensional dataset A. Both classes
% are Gaussian distributed with identy matrix
www.eeworm.com/read/152929/12073700
m trainnet.m
% Version where classes are replicated to all have same size. Samples are then presented once each in a random order
% General purpose Growing Cell Structure Visualisation and Classification
%%
f
www.eeworm.com/read/152929/12073825
m multeq.m
% Version where classes are replicated to all have same size. Samples are then presented once each in a random order
% General purpose Growing Cell Structure Visualisation and Classification
% mul