HLearn is a Haskell-based library for machine learning. Its distinguishing feature is that it exploits the algebraic properties of learning models. Every model in the library is an instance of the HomTrainer type class, which ensures that the batch trainer is a monoid homomorphism. This is a restrictive condition that not all learning models satisfy; however, it is useful for two reasons. First, this property lets us easily derive three important functions for machine learning algorithms: online trainers, parallel trainers, and fast cross-validation algorithms. Second, many popular algorithms (or variants on them) satisfy the condition and are implemented in the library. For example, the HLearn library implements the standard version of many distribution estimators and Bayesian classification, as well as homomorphic variants of perceptrons, kd-trees, decision trees, ensemble algorithms, and clustering algorithms. Furthermore, many of these learning models have additional algebraic structure that the HLearn library exploits. In particular, if a model has Abelian group structure, then we provide an even faster version of cross-validation; and if it has R-module structure, then it can handle weighted data points. The long term goal is to provide a simple interface to machine learning algorithms, so that both experts and non-experts can easily incorporate learning into the average Haskell application.
- Changes to previous version:
Initial Announcement on mloss.org.
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