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Binary-class and multi-class alternating decision trees. For more information see: Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

This project contains a logback.xml which sets up logging for brooklyn, using files supplied in the upstream brooklyn-logback-includes project. - One recommended way to configure logging on a project is to add this as an *optional* *runtime* dependency in a project's pom.xml file, so that logback.xml is on the classpath when that project is run but it is *not* exported to new/dependent projects thus allowing them to supply their own logging. - For more information see the logback.xml file in this project or the Logging section of the web site / user guide.

JavaANPR is an automatic number plate recognition software, which implements algorithmic and mathematical principles from field of artificial intelligence, machine vision and neural networks. It was created by Ondrej Martinsky in 2006 and 2007 as part of the research plan "Security-oriented research in information technology, MSM 0021630528" at Brno University of Technology.

Geomajas Graphics Editing plugin. The graphics editing plugin defines a set of interfaces and controllers for editing graphical objects in maps based on gwt-graphics. It can be used as a basis for annotations (based on SVG/VML elements) as well as to extend editing with operations like dragging/resizing. Geomajas is a component framework for building rich Internet applications (RIA) with sophisticated capabilities for the display, analysis and management of geographic information. It is a building block that allows developers to add maps and other geographic data capabilities to their web applications.

Geomajas is a component framework for building rich Internet applications (RIA) with sophisticated capabilities for the display, analysis and management of geographic information. It is a building block that allows developers to add maps and other geographic data capabilities to their web applications. Dependencies for the back-end, specifying back-end version and it's most important dependencies. When this is combined with geomajas-dep to use the snapshot back-end, then this dependency needs to be mentioned first in the pom.

This module provides versioning support for all modifications of the data contained in the LMF System (triples, resources, namespaces). Versions are accessible according to the Memento protocol using a special webservice. Versioning is triggered when a transaction finishes successfully and will store additional versioning information in the database. Note that this implies a certain performance overhead.

Extends the `fr.inria.powerapi.sensor.cpu.sensor-cpu-sigar` module in providing time spent for each CPU frequency, useful for computation taking into account the DVFS. This `Sensor` module use information provided by the [cpufreq-info](http://linux.die.net/man/1/cpufreq-info "cpufreq-info") tool under the procfs/sysfs virtual filesystems, typically used by standard Linux distributions. See also: [Man proc](http://linux.die.net/man/5/proc "proc manual").

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

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.