Examples of using Weka in English and their translations into Hebrew
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Weka(machine learning).
Advantages of Weka contain.
Or WEKA then MAF.
I think MAF, then WEKA.
Advantages of Weka include: Free availability under the GNU General Public License.
The revolutionary technology is enabling Weka.
Weka allows access to SQL databases via Java Database Connectivity and can output results returned by a database query.
The new funding will be used to accelerate Weka.
Official website at University of Waikato in New Zealand Official Weka Wiki with FAQs, HOWTOs, code-snippets, etc.
RapidMiner is a commercial machinelearning framework implemented in Java which integrates Weka.
The Cluster panel gives access to the clustering techniques in Weka, e.g., the simple k-means algorithm.
Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query.
In 2006,Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence.
The Weka. IO team has strong expertise in storage systems and we look forward to supporting them in their mission to re-define how storage is deployed.".
In the above example, we have chosen the cluster number as the x-axis,the instance number(assigned by WEKA) as the y-axis, and the"sex" attribute as the color dimension.
Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions.
Environment for DeveLoping KDD-Applications Supported by Index-Structures(ELKI)is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods.
It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a singletable that is suitable for processing using Weka.
Waikato Environment for Knowledge Analysis(Weka) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License.
This original version was primarily designed as a tool for analyzing data from agricultural domains,but the more recent fully Java-based version(Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research.
Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for others to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions.
Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka 's techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes( normally, numeric or nominal attributes, but some other attribute types are also supported).
In version 3.7.2( thus not available in the stable" book" version of Weka), a package manager was added to allow the easier installation of extension packages.[ 5] Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for other to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions.
Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka.[ 4] Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.
