kaddar

Well, yes, and I call them 'distressed phrases'

Ooh good point, --

Update: Added!

Ugh I hate it when people make this claim or link this article. If you follow one more article link deep, they say implemented some of the earlier code that came out of it, code that provided the real business value consistently.

Do you know what caused a team to win?

They Realized (1) people rate movies higher on specific days ( why would you want to implement that into your estimator? And (2) they realized that all the movies that Netflix was asking about we're movies the user was willing to rate, and because users tended to rate movies they like, this biased the results, so basically, they didn't implement the hacking the test

Ehhh the public user base perhaps isn't really so important in foss fps games because these games are most often used as LAN games when not everyone owns the same collection of games. Before tf2, Warsow was pretty much the only good fps LAN parties could use legally.

There are plenty of realistic applications for this! For example, controlling the reticle vs aim of a fps character, selecting units easily in an rts, selecting spells while looking around in an mmo, simulating multitouch, simulating button clicks to reduce cts symptoms ( so one hand draws, the other hand decides if you Re drawing and to what intensity by moving in and out of an intensity circle. )

To stick with good first step approaches, look at ngrams and nwords.

Basically, you need a reasonable feature to match similarity on. N-words are pretty easy to construct, a 2-gram would be every pair of words used in a document.

Tf-idf is a good metric with that kind of feature, because it handles well the bias of frequent words like "the"

This is a bit of an oversimplification of data mining to the point where I am not sure it is useful. Most interesting data exists in only a subset of a large feature set, where most items are irrelevant to the similarity metric. Take movies for example, if you tried to find similar movies using all features, key grip names and minor actors would unrealistically mess up your similarity score. This relates to the "curse of dimensionality".

Many data mining approaches first use a feature selection or feature extraction approach. That is, an approach which finds the relevant feature subsets, or discovers the underlying features of the data set.

Inverse Image search and the solution to the Netflix prize both used feature extraction approaches.

Does the kindle support custom fonts? It would be cool if one could experiment with this and other modern fonts focusing on readibility

I've been working on a plugin using the new sidebar extensions api, and I'm pretty sure this update broke the api :( Planning to file a ticket in the chromium bug tracker soon.

Yup, less is more. Especially on an application main page. If you explain it somewhere else, just put a huge pretty button to click to find that guide, and don't give them directions anywhere else except the FAQ/Documentation page you have.