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anirudhjoshion Show HN: I built an Android game

Awesome game! Only thing left to improve is to make a tutorial just like https://play.google.com/store/apps/details?id=pl.submachine.... - instead of a list of instructions :)

I've also just made my first android game as well called Plasma Tennis - a Real Time Fluid Dynamics Game for Android:

Show HN link: http://news.ycombinator.com/item?id=5148664

Play Store link: https://play.google.com/store/apps/details?id=com.labs.quant...

I'd love to get some feedback as well :)

anirudhjoshion Who You Are

"If I had to guess, humans end up implementing something like the reverse of a typical boosting algorithm, in that we take a bunch of too-strong pattern recognizing subunits, and then put them together into something that pits them against each other to become more robust against mis-prediction"

"Ensemble methods" seems to be what you're talking about. ( http://en.wikipedia.org/wiki/Ensemble_learning )

The application of many models put together to produce one signal to accurately predict the future.

I believe the Netflix challenge was won using ensemble methods acting in concert.

"Our final solution (RMSE=0.8712) consists of blending 107 individual results. Since many of these results are close variants, we first describe the main approaches behind them. "

( PDF paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.142... )

QIM, a large hedge fund that works futures, also uses the same model.

"In more direct language, Woodriff uses a statistical technique called the ensemble method, which is a way of mining data to produce something akin to the wisdom of crowds. A bundle of computer models, each searching for patterns in different ways, are linked together to produce a consensus statistical prediction—a sort of prediction by algorithmic committee. Scientists use the method to help predict ozone levels, for example. Woodriff uses it to help predict where futures markets are headed over a 24-hour period. His predictions are derived from four basic bits of historical pricing information: the open, close, high and low of specific markets.

Rishi Narang, whose Telesis Capital is a longtime investor in QIM, says other fund managers use similar methods and techniques. "The core idea is not so magical," Narang says. "It is how he puts it together. Getting the program correct is very challenging."

( http://www.absolutereturn-alpha.com/Article/2361672/QIMs-Jaf... )