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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).

Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

JBoss Rules provides an open source and standards-based business rules engine that enables easy business policy access, change and management. JBoss Rules is a fast and highly efficient rules engine that makes it easy for a business analyst or auditor to view your business rules as encoded in your IT application infrastructure to verify that the encoded rules indeed implement the documented business policies. JBoss Rules also supports a variety of language and decision table inputs, making it easy to quickly modify your business policies to respond to opportunities and competitive threats.

JBoss Rules provides an open source and standards-based business rules engine that enables easy business policy access, change and management. JBoss Rules is a fast and highly efficient rules engine that makes it easy for a business analyst or auditor to view your business rules as encoded in your IT application infrastructure to verify that the encoded rules indeed implement the documented business policies. JBoss Rules also supports a variety of language and decision table inputs, making it easy to quickly modify your business policies to respond to opportunities and competitive threats.

Stopwatch is a free, simple, highly extensible, Java API that allows developers to easily monitor whole application or any part of it. By default Stopwatch generate reports about hits, execution times (total, average, minimum, maximum) as well as load but it can be easily extended to measure anything else by providing custom engine. Out of the box Stopwatch uses an in-memory HSQL database. It is able to persist collected data using a "storage". There is "storage" provided to persist into HSQL database and custom "storage" can be easily integrated.

The libswresample library performs highly optimized audio resampling, rematrixing and sample format conversion operations. Specifically, this library performs the following conversions: Resampling: is the process of changing the audio rate, for example from an high sample rate of 44100Hz to 8000Hz. Audio conversion from high to low sample rate is a lossy process. Several resampling options and algorithms are available. Format conversion: is the process of converting the type of samples, for example from 16-bit signed samples to unsigned 8-bit or float samples. It also handles packing conversion, when passing from packed layout (all samples belonging to distinct channels interleaved in the same buffer), to planar layout (all samples belonging to the same channel stored in a dedicated buffer or "plane"). Rematrixing: is the process of changing the channel layout, for example from stereo to mono. When the input channels cannot be mapped to the output streams, the process is lossy, since it involves different gain factors and mixing. Various other audio conversions (e.g. stretching and padding) are enabled through dedicated options.

Janet Sudoku is a powerful Sudoku Generator and Sudoku Solver library for Java (since jdk 1.5) Android .NET (since 2.0) MONO CLS compliant. Additionally library delivers Janet Sudoku Demo App implementation in a simple form of command line tool, yet highly functional. 1. Sudoku Generator - generate Sudoku puzzles using variety of different methods. 2. Sudoku Solver - Solve any Sudoku puzzle, get the path leading to the correct solution. Check whether unique solution exists. Find all solutions if more than one present. Analyse many built-in examples of Sudoku puzzles or load any other. 3. Manipulate the board by applying transformations not affecting solution existence (i.e. rotation, reflection, permutation, swapping segments, and others). Interact with the library via consisted and well documented API. 4. Janet Sudoku Demo App - use simple, yet highly functional, command line tool to generate, solve evaluate different puzzles.

Randoop is an automatic unit test generator for Java. It automatically creates unit tests for your classes, in JUnit format. Randoop generates unit tests using feedback-directed random test generation. In a nutshell, this technique randomly, but smartly, generates sequences of methods and constructor invocations for the classes under test, and uses the sequences to create tests. Randoop executes the sequences it creates, using the results of the execution to create assertions that capture the behavior or your program and that catch bugs. Randoop has created tests that find previously unkwon errors even in widely-used libraries including Sun and IBM's JDKs. A .NET version of Randoop, used internally at Microsoft, has been used successfully by a team of test engineers to find errors in a core .NET component that has been heavily tested for years. Randoop's combination of randomized test generation and test execution results in a highly effective test generation technique.

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.