It randomly samples tweets for training, but it doesn't write back automatically. If you find one you like on the site, though, you can tweet it.
edit: I see you do actually have it at https://github.com/mattspitz/yepthatswhatshesaid - is it up to date? Thanks for sharing!
> twss.prob("was on a stiff pole");
EDIT: Counter example:
> twss.prob("that's one stiff pole");
For those interested in neural networks and Bayesian classifiers check out the brain.js library: http://harthur.github.com/brain/
It works in both node and the browser.
Interesting problem though, and nice work.
 - http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.p...
I was hoping/wondering if anyone knew of sites I could start learning about this from? I find this very interesting, and I'm sure it could be highly useful and applicable to many different types of problems...
Note to self: machine learning using node.js; what's the speed of calculations, what's the memory management in node.js, can I find pure JS implementation of SVM?
Swedish graph (täckning = recall): http://cl.ly/BJRa/pr.png
If precision & recall monotonically go down when increasing NN then it means you don't have enough training data.
Yakov Smirnoff is a structural joke. You would need to parse sentences, pattern match, transform it, and then do some kind of regression on the phrase to get its humor quotient.
The Stanford Parser for structural parsing, then some custom pattern matching and transforming code, might get you somewhere.
You can X Y <-> Y X you
and find the probability that it is an english sentence
> "You need to use Gauss-Jordan elimination."
> That's what she said.
That's what she said.
Now that I took Stanford's Machine Learning class though, I think I might just duplicate what this guy did for our bot.
You seem to have been hellbanned 132 days ago.
In other words, I'm TOTALLY going to be using this on my next project.
That said, this doesn't appear to have any Node.js specific dependencies, it could be used in any CommonJS environment.
But you're totally correct. This could've easily be written in any language.