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A library which provides custom JavaFX nodes, enhancing your UI with advanced features and seamless functionality for a superior user experience.

ANTLR.org based lexer + parser to offer a superior implementation of 'cmd-line arguments' functionality, especially via i18n

Enables custom instrumentation by providing an SDK to capture start and end times for your functions and include arbitrary data. While AppOptics provides superior out-of-the-box performance monitoring, using the SDK can allow for more detailed transaction reporting within AppOptics's user interface. For other information on our Java agent please visit http://docs.appoptics.com/kb/apm_tracing/java/. For SDK information please visit http://docs.appoptics.com/kb/apm_tracing/java/sdk/ .

Enables custom instrumentation by providing an API to capture start and end times for your functions and include arbitrary data. While TraceView provides superior out-of-the-box performance monitoring, using the API can allow for more detailed transaction reporting within TraceView's user interface. For other information on our Java agent please visit http://docs.traceview.solarwinds.com/Instrumentation/java.html. For API information please visit http://dev.appneta.com/docs/api-v2/.

Enables custom instrumentation by providing an API to capture start and end times for your functions and include arbitrary data. While AppNeta provides superior out-of-the-box performance monitoring, using the API can allow for more detailed transaction reporting within AppNeta's user interface. For other information on our Java agent please visit https://docs.appneta.com/java. For API information please visit http://dev.appneta.com/docs/api-v2/.

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).