Download JAR files tagged by 2001 with all dependencies

JsUnit is a Unit Testing framework for client-side (in-browser) JavaScript. It is essentially a port of JUnit to JavaScript. Also included is a platform for automating the execution of tests on multiple browsers and mutiple machines running different OSs. Its development began in January 2001.

JsUnit is a Unit Testing framework for client-side (in-browser) JavaScript. It is essentially a port of JUnit to JavaScript. Also included is a platform for automating the execution of tests on multiple browsers and mutiple machines running different OSs. Its development began in January 2001.

Class implementing the predictive apriori algorithm for mining association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.

Binary-class and multi-class alternating decision trees. For more information see: Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

The MAJ API was written by Richard Cartwright, with the help and support of Guillaume Belrose and Fang Ren. To contact the author, please raise an issue. The software is provided AS IS with no warranty whatsoever. The MAJ API (pronounced madge) is a pure Java API for creating, reading, manipulating and writing MXF (SMPTE ST 377), AAF structured storage and Reg-XML (SMPTE ST 2001) files. MXF files are commonly used as a container for professional media file formats and AAF is supported by a number of professional video editing packages. MXF and Reg-XML are used as part of the Interoperable Mastering Format suite of specifications.

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit. For more information see: Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud. 55(3):281-292.