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Artifacts using Weka Dev (215)

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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.
Last Release on Jun 30, 2020
Mulan is an open-source Java library for learning from multi-label datasets.
Last Release on May 26, 2014
Trainable Segmentation plugin for Fiji.
Last Release on Mar 1, 2020
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.)
Last Release on Nov 20, 2016
indigare (ital.) - investigate.
Last Release on Jul 5, 2019
This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.
Last Release on Oct 16, 2014
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.
Last Release on Mar 28, 2018


Trainable Segmentation plugin for Fiji.
Last Release on Mar 1, 2020
Trainable Segmentation plugin for Fiji.
Last Release on Mar 1, 2020
Tudarmstadt UKP DKPro TC Weka GPL
Last Release on Feb 6, 2014