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HLearn is a Haskell-based machine learning library focused on composability, algebraic structure, and performance. It provides a functional approach to building machine learning algorithms by leveraging algebraic properties such as monoids and groups. This allows for parallel, incremental, and distributed computation in a mathematically consistent way. HLearn aims to provide implementations of common algorithms like k-means, naive Bayes, and others while maintaining the expressiveness and safety of the Haskell language.
Features
- Functional and composable machine learning framework
- Implements algebraically structured learning algorithms
- Supports parallel and distributed computation
- Provides incremental learning capabilities
- Leverages Haskell's type system and category theory
- Includes classification and clustering methods
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow HLearn
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