nz.ac.waikato.cms.weka » prefuseGraph
A visualization component for displaying tree structures from those schemes that can output graphs (e.g. bayes nets). This component is available from the popup menu in the Explorer's classify. The component uses the prefuse visualization library.
更新时间: 2016-09-17 17:22nz.ac.waikato.cms.weka » prefuseGraphViewer
Knowledge Flow visualization component for displaying tree and graph structures from those schemes that can output them. This component is an alternative to the Knowledge Flow's built-in GraphViewer and uses the PrefuseTree and PrefuseGraph packages which, in turn, use the prefuse visualization library.
更新时间: 2016-08-31 08:00nz.ac.waikato.cms.weka » distributedWekaHadoopCore
This package provides loaders and savers for HDFS, plus Hadoop jobs and tasks that wrap the tasks provided in distributedWekaBase.
更新时间: 2016-08-30 17:22nz.ac.waikato.cms.weka » kfGroovy
Knowledge Flow plugin that provides a Knowledge Flow step that wraps around a Groovy script. The plugin generates a fully compilable template Groovy script that implements various Knowledge Flow interfaces. The user can fill in the methods that are necessary to accomplish the desired logic. The script is compiled at runtime and the Groovy component passes incoming events to the script and collects and passes on generated events.
更新时间: 2016-06-07 07:53nz.ac.waikato.cms.weka » bayesianLogisticRegression
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization.
更新时间: 2016-04-12 13:07nz.ac.waikato.cms.weka » largeScaleKernelLearning
This package provides filters to enable kernel-based learning from large datasets. It currently only contains the Nystroem method.
更新时间: 2016-03-01 09:31nz.ac.waikato.cms.weka » multiInstanceFilters
A collection of filters for manipulating multi-instance data. Includes PropositionalToMultiInstance, MultiInstanceToPropositional, MILESFilter and RELAGGS. For more information see: M.-A. Krogel, S. Wrobel: Facets of Aggregation Approaches to Propositionalization. In: Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003. Y. Chen, J. Bi, J.Z. Wang (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE PAMI. 28(12):1931-1947. James Foulds, Eibe Frank: Revisiting multiple-instance learning via embedded instance selection. In: 21st Australasian Joint Conference on Artificial Intelligence, 300-310, 2008.
更新时间: 2016-02-10 06:16nz.ac.waikato.cms.weka » iterativeAbsoluteErrorRegression
Provides a regression scheme that uses Schlossmacher's iteratively reweighted least squares method to fit a model that minimizes absolute error. The scheme can be used with any base learner in WEKA that performs least-squares regression
更新时间: 2016-01-25 04:58nz.ac.waikato.cms.weka » isolationForest
Class for building and using a classifier built on the Isolation Forest anomaly detection algorithm. For more information see Fei Tony Liu, Kai Ming Ting and Zhi-Hua Zhou. 2008. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 413-422.
更新时间: 2016-01-21 15:19nz.ac.waikato.cms.weka » cascadeKMeans
k-means clustering with automatic selection of k. Restarts k-means and selects the best k using the Calinski and Harabasz criterion, without cross-validation.
更新时间: 2015-12-31 11:49nz.ac.waikato.cms.weka » nifiLoader
Package for loading a directory containing MRI data in NIfTI format. The directory to be loaded must contain as many subdirectories as there are classes of MRI data. Each subdirectory name will be used as the class label for the corresponding .nii files in that subdirectory. (This is the same strategy as the one used by WEKA's TextDirectoryLoader.) Currently, the package only reads volume information for the first time slot from each .nii file. The readDoubleVol(short ttt) method from the Nifti1Dataset class (http://niftilib.sourceforge.net/java_api_html/Nifti1Dataset.html) is used to read the data for each volume into a sparse WEKA instance (with ttt=0). For an LxMxN volume (the dimensions must be the same for each .nii file in the directory!), the order of values in the generated instanc
更新时间: 2015-12-09 16:12nz.ac.waikato.cms.weka » RBFNetwork
RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many par
更新时间: 2015-01-16 10:39nz.ac.waikato.cms.weka » distributedWekaHadoop
This package provides loaders and savers for HDFS, plus Hadoop jobs and tasks that wrap the tasks provided in distributedWekaBase. Includes libraries for Hadoop 1.1.2.
更新时间: 2014-12-23 09:19nz.ac.waikato.cms.weka » consistencySubsetEval
Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.
更新时间: 2014-10-17 04:51nz.ac.waikato.cms.weka » classifierBasedAttributeSelection
This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.
更新时间: 2014-10-17 04:19| 仓库 | 个数 |
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