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This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).

BRAP is a Java and Android remoting protocol that uses native Java object serialization, encapsulated in HTTP. It aims to be an alternative to Spring HttpInvoker and Spring Security especially when you don't need or want the dependencies of Spring in your client, for example when building a rich client app or an Android app where size is important. The authentication mechanism lets you use your own domain objects as credentials. Server configuration can be expressed solely in web.xml or by subclassing the ProxyServlet but there is also an optional SpringProxyServlet available for seamless Spring Integration. BRAP focuses on being easy to use, small in size (9 kb), yet powerful and extensible. The client uses a pluggable TransportProvider with default implementations for HttpURLConnection and Apache HttpClient.

OpenCL4Java is a thin Java wrapper around OpenCL's C API. It uses JNA as its interop layer library, which means it works on all of the (many) JNA-supported platforms (see http://jna.dev.java.net/). It is autogenerated by JNAerator (http://jnaerator.googlecode.com/), so updates to newer OpenCL specs are a matter of seconds. Note that OpenCL4Java is used by JavaCL, an Object-Oriented API that presents OpenCL in a much more practical, powerful and idiomatic way to Java. For more info, please visit http://code.google.com/p/nativelibs4java/wiki/OpenCL.

This plugin will support various tasks within the Weblogic 8.1, 9.x, 10.x and 12.x environment. This version starts to support weblogic 10 and 11. The mojo supports such tasks as deploy, undeploy,clientgen,servicegen, and appc are supported as well as many others. The plugin uses exposed API's that are subject to change but have been tested in 8.1 SP 4-6 and 9.0 - 9.2 MP3, 10.x. There are two versions of the plugin to support the two environments based on differences in the JDK. The 9.x version is currently being refactored to support the standard JSR supported deployment interface. The code used in the plugin has been contributed to the Cargo project however to date has not be integrated into the codebase. Please feel free to suggest improvements or help support this plugin effort.

Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.

The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. The current version can be used for classification using numeric predictors. For more information see: I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support. In: 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003; V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas, V. Petridis (2003). Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.

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.