Download nz.ac.waikato.cms.weka.thirdparty JAR files with all dependencies

NimROD Look and Feel is an extension of the Metal Look and Feel, so it should be a portable look and feel. This POM was contributed by Rob Manning and can be used to build this laf from the original source code if the source code is structured according to Maven conventions.

Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.

FURIA: Fuzzy Unordered Rule Induction Algorithm. For details please see: Jens Christian Huehn, Eyke Huellermeier (2009). FURIA: An Algorithm for Unordered Fuzzy Rule Induction. Data Mining and Knowledge Discovery.

An extension of RTextArea that adds syntax highlighting of certain programming languages to its list of features.

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.)

Pulls down protobuf, patches it, compiles, and then relocates/shades.

Mavenized build of the XenServer SDK for Java.

The Napkin Look and Feel is a pluggable look and feel for Java Swing that makes the UI look as if it was hand-sketched on a napkin. This provisional look is designed tfor use during project development so that provisional parts of the UI look provisional, but of course you may use it for anything you like.

Java library and GUI for supporting Model-Based Testing. The models are extended finite state machines (EFSM) that are written in Java.

Provides a time series forecasting environment for Weka. Includes a wrapper for Weka regression schemes that automates the process of creating lagged variables and date-derived periodic variables and provides the ability to do closed-loop forecasting. New evaluation routines are provided by a special evaluation module and graphing of predictions/forecasts are provided via the JFreeChart library. Includes both command-line and GUI user interfaces. Sample time series data can be found in ${WEKA_HOME}/packages/timeseriesForecasting/sample-data.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

Simple learning schemes for educational purposes (Prism, Id3, IB1 and NaiveBayesSimple).

This package provides loaders and savers for HDFS, plus Hadoop jobs and tasks that wrap the tasks provided in distributedWekaBase. Includes libraries for Hadoop 1.1.2.