Follow the Data

A data driven blog

Some interesting new algorithms

Just wanted to note down some new algorithms that I came across for future reference. Haven’t actually tried any of these yet.

  • LIBFMM, a library for Field-aware Factorization machines. Developed by a group at National Taiwan University, this technique has been used to win two Kaggle click-through competitions. (Criteo, Avazu)
  • Random Bits Regression, a “strong general predictor for big data” (paper). “This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized¬† linear/logistic regression on those intermediate/derived features to predict the outcome.
  • BIDMach, a CPU and GPU-accelerated machine learning library that shows some amazing benchmark results compared to Spark, Vowpal Wabbit, scikit-learn etc.

And another one which is not as new, but which I wanted to highlight because of a nice blog post about interactions and generalization by David Chudzicki:


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