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This module is in charge of generating required classes to bind a java interface toward a remote webservice. It uses th org.apache.cxf:cxf-codegen-plugin to generate classes given the WSDL file saved in src/main/wsdl. To keep things simple, the wsdl file is saved here: more smart approaches might be adopted if saving the WSDL file causes troubles.

The CKIP (Chinese Knowledge and Information Processing) is a online service for separate Chinese sentence. The service maintain by [CKIP group](http://godel.iis.sinica.edu.tw/CKIP/) in [Sinica](http://www.sinica.edu.tw). CKIPClient-Java is a client library that simplify the procedure of TCP scoket communication between client and CKIP server. Developer can use CKIPClient-Java to access CKIP service more easily. The CKIPClient-Java licensed under Apache License Version 2.0. CKIP 是中研院詞庫小組開發的中文斷詞系統，此系統採用線上服務模式，採用 TCP Scoket 傳送文本到 Server 進行斷詞，再接收斷詞結果。 此專案是將上述斷詞流程採用 Java 實作，提供使用者方便地使用 CKIP 服務。本人並非中研院成員或是詞庫小組相關人士，本人只是將之前撰寫的 Client 端程式碼公開，並嘗試以不同的程式語言實做。所有中文斷詞相關技術及問題請洽中研院詞庫小組。 CKIPClient-Java 採用 Apache License Version 2.0 釋出

Niowire is a non blocking socket server framework for java. It makes the task of writing and implementing a highly scalable server capable of many simultaneous connections easy. All that has to be written to use it are a Serializer to parse the binary stream into objects (or you can use one of the built in ones), an Inspector which filters, authenticates and modifies packets packets (or again, use one of the built in ones) and one or more services which uses the data packets (more built in ones here too!)

Jodd is an open-source Java utility library and set of frameworks. Jodd tools enriches JDK with many powerful and feature rich utilities. It helps with everyday task, makes code more robust and reliable. Jodd frameworks is set of lightweight application frameworks, compact yet powerful. Designed following the CoC, DRY and SCS principles, it makes development simple, but not simpler.

Java Validation Framework (not a JSR-303 implementation). This framework allows to modify (correct) test values; supports validation of localized values; allows to convert values into and from text representation; formats error messages, depending on the context; supports metadata, which allows to check any entity types (not only Java Beans or POJOs) and more. Also this framework has long list of predefined validation components.

Ipsedixit is a lightweight provider of arbitrary data, mocks and dummies for your Java testing pleasure. It is designed to reduce the amount of boilerplate setup code in your tests, allowing you to spend more time solving more interesting problems. Check out the wiki for the latest docco and examples.

Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)

Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences contained in the set can be determined by the respective option. Furthermore, the set of output results can be restricted by specifying one or more attributes that have to be contained in each element/itemset of a sequence. For further information see: Ramakrishnan Srikant, Rakesh Agrawal (1996). Mining Sequential Patterns: Generalizations and Performance Improvements.