搜索资源列表
judge_binaries
- 自动地分类和聚集文档，可以选择作为一个Web服务。这个程序完全是用Java编写的，利用了 Weka机器学习工具包。-automatic classification and gathering documents, we can choose as a Web service. This procedure is completely written in Java, Weka use of machine learning kits.
PrefixSpan
- java版的PrefixSpan算法实现，文件里包含了详细的文档说明，还有示例。-PrefixSpan algorithm. The document containing a detailed descr iption and an example.
BayesFileClassify
- 文本分类是在文档所包含的文本基础上， 把给定的文档分配到固定类别集合中某一个类别的任务。这个任务中常常用到朴素贝叶斯模型。-Text classification are contained in the document text, based on the given document category assigned to a fixed set of a certain type of mission. This task is often used Naive Bayes model
ResearchofESTOOLS
- 专家系统编程工具选择的介绍非常详细的文档，完全自己总结的，做毕业设计的同学们可以参考。-Expert system programming tools of choice to introduce a very detailed document, complete their lessons, so students graduating from the design can refer to.
textclusterr
- 文档分类，用K均值，加入了K的选择算法，不用人为设定聚类个数-Document classification, using K-means, joined the K of the selection algorithm, not the number of artificial clustering
TFIDF
- 用于计算文档向量的TFIDF权值，代码使用Java语言写的-Used to calculate the document vector of TFIDF weight, code written using the Java language
ACO
- 蚁群算法的原理，C实现，JAVA实现文档-The principle of ant colony algorithm, C implementation, JAVA implementation
SRTP1.0
- javaSwing实现的web文档聚类方法研究，不同权值与精度，直接输入新闻网址，可以自动解析并聚类web文档-javaSwing implemented method of web document clustering, different weights and precision, direct input news sites, you can automatically parse and web document clustering
SVM
- 支持向量机实现的文档分类，适宜初学者，JAVA实现。-Implementation of support vector machine classification of the document, suitable for beginners
JavaNNS_Source
- 人工神经网络的java实现.基于SNNS的改进项目，提供完整的文档和使用环境-JavaNNS actually consists of two separate modules, the Java GUI and the SNNS kernel written in C. This source code distribution of JavaNNS supplies precompiled kernels and kernel interfaces for several pla
数据挖掘2
- 本实验文档包括了数据挖掘的分类算法，FP-growth和Apriori 算法的java程序设计实现。(This report is about the classification algorithm, it contains FP-growth algorithm and Apriori algorithm.)