Download JAR files tagged by further with all dependencies

iServe Integrated Engine provides a complete an integrated local version of iServe to be used within Java Applications. This module provides integrated programmatic access for the deployment and interaction with iServe engines. iServe is what we refer to as service warehouse which unifies service publication, analysis, and discovery through the use of lightweight semantics as well as advanced discovery and analytic capabilities. iServe provides the typical features of service registries and additional functionality that exploits service descriptions, service annotations and further data gathered and derived from the analysis of these descriptions, data crawled from the Web, periodic monitoring and user activities.

iServe is what we refer to as service warehouse which unifies service publication, analysis, and discovery through the use of lightweight semantics as well as advanced discovery and analytic capabilities. iServe provides the typical features of service registries and additional functionality that exploits service descriptions, service annotations and further data gathered and derived from the analysis of these descriptions, data crawled from the Web, periodic monitoring and user activities. iServe builds upon lessons learnt from research and development on the Web and on service discovery algorithms to provide a generic semantic service registry able to support advanced discovery over both Web APIs and WSDL services described using heterogeneous formalisms.

The Jenkins Maven Plugin allows to use the Jenkins CLI (command line interface) from within a Maven build. It allows the execution of any command supported by the CLI, see https://wiki.jenkins-ci.org/display/JENKINS/Jenkins+CLI for further information. Therefore, it downloads the current jenkins-cli.jar from ${project.ciManagement.url}/jnlpJars/jenkins-cli.jar and uses that artifact for interacting with the Jenkins server. The architecture of the Jenkins Maven Plugin follows the UNIX philosophy "do one thing and do it well": it does not provide any logic for specific tasks, for instance creating job configuration files. This can be done with another, more appropriate plugin and its result can then be specified as stdin to the Jenkins Maven Plugin.

Jadex XML is an XML data binding framework for Java and also for other representations. The main idea of Jadex XML is that neither the XML-Schema on the one side nor the Java classes on the other side should define the binding. Instead, a separate mapping between both is used as a mediation. This allows designing the XML representation independent of the Java side but still being able to connect both as desired. This idea was first put forward by the JiBX data binding framework. Jadex XML pushes it further by combining it with the configuration by exception principle. The framework can detect obvious correspondences between both sides automatically and only needs configuration information when translations are necessary. The configuration information is currently specified directly in form of Java configuration classes.

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).

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.