The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.
This open source benchmarking framework allows you to build your own P2P learning algorithm and evaluate it in a simulated but realistic -- where you can model message delay, drop or churn -- networked environment. Moreover it contains the prototype implementations of some well-known machine learning algorithms like SVM and Logistic Regression.
Experiments in applying evolutionary algorithms, neural networks and other AI/CI/ML algorithms to Super Mario Bros.MarioAI is a benchmark for machine learning and artificial intelligence based on ...
The suite of fast incremental algorithms for machine learning (sofia-ml) can be used for training models for classification, regression, ranking, or combined regression and ranking. Several diffe...
RF-ACE is an efficient implementation of a robust machine learning algorithm for uncovering multivariate associations, building predictors, and predicting novel data, either with classification or...