blocks|key|2883381|text|我绝对推荐Weka，这是一个用Java编写的开源数据挖掘软件：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2883382|2883383|+Weka是用于数据挖掘任务的机器学习算法的集合。这些算法可以直接应用于数据集，也可以从您自己的Java代码中调用。Weka包含用于数据预处理、分类、回归、聚类、关联规则和可视化的工具。它也非常适合于开发新的机器学习方案。|blockquote|2883384|2883385|2883386|如上所述，它附带了一系列不同的分类器，如SVM、Winnow、C4.5、朴素贝叶斯(当然)和更多(参见API+doc)。请注意，众所周知，在垃圾邮件检测或文本分类领域，许多分类器具有比朴素贝叶斯更好的性能。|2883387|此外，Weka为您带来了一个非常powerful+GUI的…|2883388|entityMap|0|LINK|mutability|MUTABLE|url|http://www.cs.waikato.ac.nz/ml/weka/|1|http://en.wikipedia.org/wiki/Support_vector_machine|2|http://en.wikipedia.org/wiki/Winnow_%2528algorithm%2529|3|http://en.wikipedia.org/wiki/C4.5_algorithm|4|http://weka.sourceforge.net/doc/|5|http://www.cs.waikato.ac.nz/~ml/weka/gui_explorer.html^0|5|4|0|0|0|0|0|0|K|3|1|O|6|2|V|4|3|1F|7|4|0|G|C|5|0^^$0|@$1|2|3|4|5|6|7|15|8|@]|9|@$A|16|B|17|1|18]]|C|$]]|$1|D|3|-4|5|6|7|19|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|1A|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|1B|8|@]|9|@]|C|$]]|$1|I|3|-4|5|6|7|1C|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1D|8|@]|9|@$A|1E|B|1F|1|1G]|$A|1H|B|1I|1|1J]|$A|1K|B|1L|1|1M]|$A|1N|B|1O|1|1P]]|C|$]]|$1|L|3|M|5|6|7|1Q|8|@]|9|@$A|1R|B|1S|1|1T]]|C|$]]|$1|N|3|-4|5|6|7|1U|8|@]|9|@]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]|V|$5|Q|R|S|C|$T|W]]|X|$5|Q|R|S|C|$T|Y]]|Z|$5|Q|R|S|C|$T|10]]|11|$5|Q|R|S|C|$T|12]]|13|$5|Q|R|S|C|$T|14]]]]

I definitely recommend <a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="noreferrer">Weka</a> which is an Open Source Data Mining Software written in Java:

<blockquote>
 Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
</blockquote>

As mentioned above, it ships with a bunch of different classifiers like <a href="http://en.wikipedia.org/wiki/Support_vector_machine" rel="noreferrer">SVM</a>, <a href="http://en.wikipedia.org/wiki/Winnow_%28algorithm%29" rel="noreferrer">Winnow</a>, <a href="http://en.wikipedia.org/wiki/C4.5_algorithm" rel="noreferrer">C4.5</a>, Naive Bayes (of course) and many more (see the <a href="http://weka.sourceforge.net/doc/" rel="noreferrer">API doc</a>).
Note that a lot of classifiers are known to have much better perfomance than Naive Bayes in the field of spam detection or text classification.

Furthermore Weka brings you a very <a href="http://www.cs.waikato.ac.nz/~ml/weka/gui_explorer.html" rel="noreferrer">powerful GUI</a>…

blocks|key|699012|text|也许是https://ci-bayes.dev.java.net/或http://www.cs.cmu.edu/~javabayes/Home/node2.html|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|699013|我也从来没玩过。|699014|entityMap|0|LINK|mutability|MUTABLE|url|https://ci-bayes.dev.java.net/|1|http://www.cs.cmu.edu/~javabayes/Home/node2.html^0|3|U|0|Y|1C|1|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

Maybe <a href="https://ci-bayes.dev.java.net/" rel="nofollow noreferrer">https://ci-bayes.dev.java.net/</a> or <a href="http://www.cs.cmu.edu/~javabayes/Home/node2.html" rel="nofollow noreferrer">http://www.cs.cmu.edu/~javabayes/Home/node2.html</a>?

I never played with it either.

blocks|key|694310|text|查看Programming+Collective+Intelligence的第6章|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|694311|entityMap|0|LINK|mutability|MUTABLE|url|https://rads.stackoverflow.com/amzn/click/com/0596529325^0|2|Z|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Check out Chapter 6 of <a href="https://rads.stackoverflow.com/amzn/click/com/0596529325" rel="noreferrer" rel="nofollow noreferrer">Programming Collective Intelligence</a>

blocks|key|694300|text|下面是贝叶斯过滤在C#中的一个实现：A+Naive+Bayesian+Spam+Filter+for+C#+(托管在CodeProject上)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|694301|entityMap|0|LINK|mutability|MUTABLE|url|http://www.codeproject.com/KB/recipes/BayesianCS.aspx^0|I|Z|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Here is an implementation of Bayesian filtering in C#: <a href="http://www.codeproject.com/KB/recipes/BayesianCS.aspx" rel="nofollow noreferrer">A Naive Bayesian Spam Filter for C#</a> (hosted on CodeProject).

blocks|key|5138541|text|nBayes+-另一个托管在CodePlex上的C#实现|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|5138542|entityMap|0|LINK|mutability|MUTABLE|url|http://codecube.net/2009/05/bayesian-filtering-with-c/^0|0|6|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

<a href="http://codecube.net/2009/05/bayesian-filtering-with-c/" rel="nofollow noreferrer">nBayes</a> - another C# implementation hosted on CodePlex

blocks|key|5138252|text|在法语中，但您应该能够找到下载链接:)+PHP+Naive+Bayesian+Filter|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|5138253|entityMap|0|LINK|mutability|MUTABLE|url|http://xhtml.net/scripts/PHPNaiveBayesianFilter^0|K|P|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

In French, but you should be able to find the download link :)
<a href="http://xhtml.net/scripts/PHPNaiveBayesianFilter" rel="nofollow noreferrer">PHP Naive Bayesian Filter</a>

I was wondering if there is any good and clean object-oriented programming (OOP) implementation of Bayesian filtering for spam and text classification? This is just for learning purposes.

Object Oriented Bayesian Spam Filtering?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我想知道是否有用于垃圾邮件和文本分类的贝叶斯过滤的好的和干净的面向对象编程(OOP)实现？这只是为了学习的目的。

问面向对象的贝叶斯垃圾邮件过滤？
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