Artifacts using weka-dev version 3.9.6

Trainable Segmentation plugin for Fiji.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

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.

The mcib3d library is the core library for most of the mcib3d plugins.

The Labkit image segmentation tool for Fiji.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. ADAMS fork.

ClearTK wrapper for the WEKA machine learning library

The implementation of the pixel classification algorithm, that is used the Labkit image segmentation plugin for Fiji.

This JAR file is used for implementing the Level-Site Privacy-Preserving Decision Trees (PPDT). See the paper "Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees" which describes the implementation and performance gains this approach has in avoiding the conversion to a complete binary tree as in the Joye and Salehi paper.

Mzmine3