<story><title>Organizing My Emails with a Neural Net</title><url>http://www.andreykurenkov.com/writing/organizing-my-emails-with-a-neural-net/</url></story><parent_chain></parent_chain><comment><author>tyingq</author><text>Popfile is worth looking at if this was interesting to you.&lt;p&gt;It is a general purpose naïve bayesian email classifier that you can integrate with almost any email system.&lt;p&gt;They took some of the concepts in the article mentioned here and expanded on them a bit.&lt;p&gt;For example, they have the idea of &amp;quot;pseudowords&amp;quot;[1] so that you&amp;#x27;re working with more than just the words in the email. Like html:td for example...it expands to the number html table cells in an email, which might help with choosing a bucket.&lt;p&gt;[1]&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;getpopfile.org&amp;#x2F;docs&amp;#x2F;faq:pseudowords&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;getpopfile.org&amp;#x2F;docs&amp;#x2F;faq:pseudowords&lt;/a&gt;</text></comment>
<story><title>Organizing My Emails with a Neural Net</title><url>http://www.andreykurenkov.com/writing/organizing-my-emails-with-a-neural-net/</url></story><parent_chain></parent_chain><comment><author>thibauts</author><text>Is it common practice to use the most frequent words as features ? It looks like they don&amp;#x27;t carry much information, by definition. As a first naive approach, I&amp;#x27;d rank the words by the inverse of how many categories they appear in, factor in the overall frequency, with a weight or something more clever and then take the top N.&lt;p&gt;Well, thinking more about it leads me to tf-idf and naive bayes (of course), at which point you pretty much already have a classifier. So it seems feature selection &lt;i&gt;is&lt;/i&gt; learning in itself and defines the maximum accuracy you&amp;#x27;ll be able to reach ? This is border philosophical but I&amp;#x27;d love to read more about these matters. Pointers welcome !</text></comment>