machine-learning

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This module contains the Kotlin API for building, training, and evaluating the Deep Learning models.

Module for experimental WEKA code.

Deep Java Library (DJL) Engine Adapter for PyTorch

pytorch_android_lite pytorch android api

Bluima OpenNLP integration

ImageJ/TensorFlow integration.

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.)

Apache OpenNLP Tools

High-level abstractions for training and serving TensorFlow models.

The MEKA project provides an open source implementation of methods for multi-label classification and evaluation. It is based on the WEKA Machine Learning Toolkit. Several benchmark methods are also included, as well as the pruned sets and classifier chains methods, other methods from the scientific literature, and a wrapper to the MULAN framework.