代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
代码结果 5,352
www.eeworm.com/read/137284/13335192
cpp main.cpp
/*=============================================================================
|
| Description: This program is an implementation of the Association Rule
| learning algorithm(Apriori).
www.eeworm.com/read/153204/12052115
cpp main.cpp
/*=============================================================================
|
| Description: This program is an implementation of the Association Rule
| learning algorithm(Apriori).
www.eeworm.com/read/192817/5150086
cs mainform.cs
// AForge Framework
// Classifier using Delta Rule Learning
//
// Copyright
www.eeworm.com/read/304754/3786110
cs mainform.cs
// AForge Framework
// Classifier using Delta Rule Learning
//
// Copyright
www.eeworm.com/read/431145/1909784
cs mainform.cs
// AForge Framework
// Classifier using Delta Rule Learning
//
// Copyright
www.eeworm.com/read/417673/10980915
m hwmain.m
function out = hwmain(case_n, random_seed)
%HWMAIN Performance evaluation of MLP learning strategies.
% This file is used to evaluate various MLP learning strategies
% for one of the homework in CS
www.eeworm.com/read/467198/7020122
m hwmain.m
function out = hwmain(case_n, random_seed)
%HWMAIN Performance evaluation of MLP learning strategies.
% This file is used to evaluate various MLP learning strategies
% for one of the homework in CS
www.eeworm.com/read/244790/12843842
m rpcl.m
% MATLAB implementation of Rival-Penalized Competitive Learning (RPCL)
% Source:
% L. Xu, A. Krzyzak and E. Oja 1993, "Rival penalized competitive
% learning for clustering analysis, RBF net
www.eeworm.com/read/143498/12870628
m hwmain.m
function out = hwmain(case_n, random_seed)
%HWMAIN Performance evaluation of MLP learning strategies.
% This file is used to evaluate various MLP learning strategies
% for one of the homework in CS
www.eeworm.com/read/130490/14189992
c ngram.c
/* Copyright (C) 2001-2002 Mikael Ylikoski
* See the accompanying file "README" for the full copyright notice */
/**
* @file
* N-gram learning learning algorithm.
*
* Should be used with ngram t