Haskellの機械学習ライブラリHLearn - Qiita
import HLearn.Models.Distributions import HLearn.Algebra.Models.HomTrainer dataset = [1,2,3,4,5,6] dist = train dataset :: Normal Integer Integer -- 引用元： http://izbicki.me/blog/functors-and-monads-for-analyzing-data -- いろいろな色のビー玉の定義 data Marble = Red | Pink | Green | Blue | White deriving (Read,Show,Eq,Ord) bagOfMarbles = [ Pink,Green,Red,Blue,Green,Red,Green,Pink,Blue,White ] -- カテゴリーデータ（このカテゴリーと
scikit-learn: machine learning in Python
Simple and efficient tools for predictive data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable - BSD license Classification Identifying which category an object belongs to. Applications: Spam detection, image recognition. Algorithms: Gradient boosting, nearest neighbors, random forest, logistic regression, an
Mulan: A Java library for multi-label learning
Home Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic an
Homepage — Modular toolkit for Data Processing (MDP)
News 09.03.2016 MDP 3.5 released! This is a bug-fix release Note that from this release MDP is in maintenance mode. 13 years after its first public release, MDP has reached full maturity and no new features are planned in the future. If you plan to do serious machine learning in Python, use sklearn. Note though that some algorithms, notably SFA and Growing Neural Gas, are only available in MDP. We
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