4/6 Looking for introductory material on SVM, C4., Ripper, and Naive
Bayes. Most of the junk on google are too deep for comprehension.
I just need introductory material. ok thx
\_ http://paulgraham.com / google "A Plan For Spam"
\_ where did you hear about all of these terms? did you learn about
them with regards to spam filtering? naive bayes is a specific
text classification strategy. SVM and C4 are generic classifiers.
\_ which one is most powerful?
\_ dude, you're asking a hardcore computer science theory question.
Most of the people here are sysadms (they don't even know how to
do FFT in their sleep). Wrong place.
\_ Naive Bayes is hardcore if you call counting hardcore. SVM
stuff isn't really CS, although AI people use it (they are big
on separating things with planes). Also, the poster above is
wrong, Naive Bayes is also a general classifier.
An interesting thing about classifiers is that
you don't want the one that's 'most powerful' because it will
overfit. You want one that's barely smart enough to work.
Statisticians call this 'bias/variance tradeoff'. -- ilyas
\_ I'm a sysadmin. I graduated with a ~3.5 in CS. I sysadmin
because the options are 1) coding which I hate and it's all
going to India anyway, 2) I don't want a MS/PhD because I like
to eat and pay my bills, 3) I work for about 1.5 hours a day and
get paid more than almost every coder I've ever met. Instead of
being bitter that you work harder for less while always having
to worry about when your job is going offshore, you should learn
some industry best practices and get paid to surf like me. What
good is FFT in your sleep if in your waking hours you're looking
for a non-existent job in your field? None of this applies to
the PhDs and most MS people who can always teach if they must.
\_ FFT?
\_ Final Fantasy Tactics!
\_ Fast-fourier transform.
\_ this is very sad. |
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