Search Results for "digital library source code"

Tribuo* is a machine learning library written in Java. It provides tools for classification, regression, clustering, model development, and more. It provides a unified interface to many popular third-party ML libraries like xgboost and liblinear. With interfaces to native code, Tribuo also makes it possible to deploy models trained by Python libraries (e.g. scikit-learn, and pytorch) in a Java program. Tribuo is licensed under Apache 2.0. Remove the uncertainty around exactly which artifacts...

LAML is a stand-alone pure Java library for linear algebra and machine learning. The goal is to build efficient and easy-to-use linear algebra and machine learning libraries. The reason why linear algebra and machine learning are built together is that full control of the basic data structures for matrices and vectors is required to have fast implementation for machine learning methods. Additionally, LAML provides a lot of commonly used matrix functions in the same signature to MATLAB, thus...

JML is a machine learning library in Java, it is a pure Java package, and thus is cross-platform. The goal of JML is to make machine learning methods very easy to use and speed up code conversion from MATLAB to Java. Please be noted that JML has been replaced by LAML.

libVMR is a class library written in Java which implements code generator for group method of data handling - GMDH. The library is intended for users, with machine learning skills. libVMR provides an effective framework for the research and development of data mining and predictive analytics. libVMR is based on the most popular neural network model with a higher generalization ability from kernel tricks - vector machine by Reshetov (VMR). The library has been designed to learn from...

The JaCHMM - the Java Conditioned Hidden Markov Model library - is a complete implementation of a CHMM in Java ready to use either on command line or as a module. The JaCHMM is licenced under the BSD licence. It gives an implementation of the Viterbi, Forward-Backward, Baum-Welch and K-Means algorithms, all adapted for the CHMM. JaCHMM is based on the JaHMM and also designed to achieve reasonable performance without making the code unreadable. Consequently, it offers a good way of...