搜索资源列表
树木分类专家系统
- 这是一个基于WEB的树木分类专家系统asp源代码,对于初学者特别有帮助!-This is a Web-based expert system for classification trees asp source code, especially for beginners help!
englishparsing_src
- 英语句子自然语言处理统计分析例子 Statistical parsing of English sentences Shows how to generate parse trees for English language sentences using a C# port of OpenNLP, a statistical natural language parsing library.-English sentence natural language processing
fastDNAml_1.2.2p
- fastDNAml is an attempt to solve the same problem as DNAML, but to do so faster and using less memory, so that larger trees and/or more bootstrap replicates become tractable. Much of fastDNAml is merely a recoding of the PHYLIP 3.3 DNAML progra
decision-trees
- 决策树lisp代码 决策树lisp代码-decision tree lisp code decisio n tree lisp code
icsiboost-0.3.tar
- Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the
C50
- 功能强大的决策树分类算法，是C4.5的改进版本，但在精度，速度和内存开销上均有了很大的改进。目前由rulequest公司管理，其可执行程序版本为商业版本，此GPL许可的源代码对外发布。-Both C4.5 and C5.0 can produce classifiers expressed either as decision trees or rulesets. In many applications, rulesets are preferred because they are simp
NeC45
- NeC4.5 is a variant of C4.5 decision tree, which could generate decision trees more accurate than standard C4.5 decision trees, through regarding a neural network ensemble as a pre-process of C4.5 decision tree.
DecisionTrees
- 决策树的C/C++源码实现, 机器学习的代码-Decision Trees implementation in C/C++, Machine Learning Code
MEGA4
- 包含clustal W功能,可以构建系统进化树、比对基因序列等功能，可以做BLAST搜索-Contains clustal W function, one can construct phylogenetic trees, and other functions than on the gene sequence, BLAST searches can be done
CARTClassificationandRegressionTrees
- Classification and Regression Tree的ppt介绍，简称CART，即分类回归树。-Ppt Classification and Regression Tree of the introduction, referred to as CART, the classification and regression trees.
ID3
- ID3算法是机器学习部分的重要算法，程序实现ID3算法的基本思想，生成决策树。-ID3 algorithm is an important part of machine learning algorithms, the program to achieve the basic idea of ID3 algorithm to generate decision trees.
C4.5)
- C4.5源代码,为数据挖掘中的C4.5的详细源代码，可以生成决策树-C4.5 source code, as in the C4.5 data mining details the source code, you can generate decision trees
Kode-Program-Algoritma-C4.5
- C4.5 is an algorithm used to generate a decision tree. C4.5 is an extension of Quinlan s earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical class
Fuzzy-logic
- 模糊逻辑在树木检测中的应用，可以做算法的参考，效果不错-Fuzzy logic trees, detection, can do the reference algorithm, the effect is good
IrisDC06
- 分类是数据挖掘 、机器学习 和模式识别 中一个重要的研究领域。分类的目的是学会一个分类模型 (称作分类器)，该模型能把未知类别的数据项映射到给定类别中。目前发展较成熟的几种分类算法 如决策树、神经网络、贝叶斯方法、遗传算法等。分类具有广泛的应用，例如医学诊断、信用卡系统的信用分级、图像模式识别等。本毕业设计通过使用鸢尾属植物(IRIS)数据集，对当前数据挖掘中具有代表性的优秀分类算法进行分析和比较，总结出了各种算法的特性，为使用者选择算法或研究者改进算法提供了依据。-Classificatio
test_draworb0
- 高级信息提取 基于专家知识的决策树分类：规则获取（经验总结、数据挖掘如c4.5 cart算法）、规则定义以及构建决策树 -Advanced information extraction based on expert knowledge of the decision tree classification: the rules to get (lessons learned, data mining algorithms such as c4.5 cart), definit
machine-learningPPT
- 书中主要涵盖了目前机器学习中各种最实用的理论和算法，包括概念学习、决策树、神经网络、贝叶斯学习、基于实例的学习、遗传算法、规则学习、基于解释的学习和增强学习等-The book mainly covers the current variety of the most practical machine learning theory and algorithms, including the concept of learning, decision trees, neural network
NeC45
- 利用经典的C45算法实现特征集成选择，获得集成树分类规则-Use NeC4.5 to generate classification trees.
Classification-and-regression-trees
- 数据挖掘教程：分类与回归树模型的教程介绍-Classification and regression trees documentation
Decision-Trees
- Machine Learning Decision Trees