Download JAR files tagged by 1995 with all dependencies

Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules). For more information, see Brent Martin (1995). Instance-Based learning: Nearest Neighbor With Generalization. Hamilton, New Zealand. Sylvain Roy (2002). Nearest Neighbor With Generalization. Christchurch, New Zealand.

This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

An implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions. For more information about Ripple-Down Rules, see: Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst. 5(3):211-228.

Jetty is an open-source, standards-based, full-featured web server implemented entirely in Java. It is released under the Apache 2.0 licence and is therefore free for commercial use and distribution. First created in 1995, Jetty has benefitted from input from a vast user community and consistent and focused development by a stable core of lead developers. There are many more examples of Jetty in action on the Jetty Powered Page that has selections from among the tens of thousands of production Jetty instances. However, as Jetty aims to be as unobtrusive as possible, countless websites and products are based around Jetty, but Jetty is invisible!