Download nz.ac.waikato.cms.weka.thirdparty JAR files with all dependencies
SVMAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.2)
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.
org.openide.awt from group com.github.veithen.visualwas.thirdparty (version 2.0.0)
org.netbeans.modules.options.api from group com.github.veithen.visualwas.thirdparty (version 3.0.0)
2 downloads
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.7)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
java-cup from group net.sf.squirrel-sql.thirdparty.non-maven (version 11a)
CUP is a system for generating LALR parsers from simple specifications. It serves the same role as
the widely used program YACC [1] and in fact offers most of the features of YACC. However, CUP is
written in Java, uses specifications including embedded Java code, and produces parsers which are
implemented in Java.
asm from group org.nuiton.thirdparty (version 1.5.4-snapshot)
2 downloads
rlforj from group fr.irit.smac.thirdparty.net.sf.rlforj (version 0.3)
This is a modular easy to use Java library for developing Roguelike Games.
2 downloads
org-openide-util-lookup from group uk.gov.nationalarchives.thirdparty.netbeans (version 7.2)
NetBeans OpenIDE utilities lookup
Group: uk.gov.nationalarchives.thirdparty.netbeans Artifact: org-openide-util-lookup
Show documentation Show source
Show documentation Show source
2 downloads
mason from group fr.irit.smac.thirdparty.edu.gmu.cs (version 18)
MASON is a fast discrete-event multiagent simulation library core in Java, designed to be the foundation for large custom-purpose Java simulations, and also to provide more than enough functionality for many lightweight simulation needs. MASON contains both a model library and an optional suite of visualization tools in 2D and 3D.
oneClassClassifier from group nz.ac.waikato.cms.weka (version 1.0.4)
Performs one-class classification on a dataset.
Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class.
Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes.
For more information, see:
Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.
2 downloads
org.openide.util from group com.github.veithen.visualwas.thirdparty (version 1.0.0)
zendesk-java-client from group com.cloudbees.thirdparty (version 0.7.0)
Java client for the Zendesk API
predictiveApriori from group nz.ac.waikato.cms.weka (version 1.0.4)
Class implementing the predictive apriori algorithm for mining association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.
userClassifier from group nz.ac.waikato.cms.weka (version 1.0.3)
Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit.
For more information see:
Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud. 55(3):281-292.
probabilityCalibrationTrees from group nz.ac.waikato.cms.weka (version 1.0.0)
Provides probability calibration trees (PCTs) for local calibration of class probability estimates. To achieve
calibration of a base learner, the PCT class must be used as the meta learner in the CascadeGeneralization class, which
is also included in this package. The classifier to be calibrated must be used as the base learner in the CascadeGeneralization class.
The CascadeGeneralization class can also be used independently to perform CascadeGeneralization for ensemble learning.
The code for PCTs is largely the same as the LMT code for growing logistic model trees. For more details, see
the ACML paper on probability calibration trees.
2 downloads
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