nz.ac.waikato.cms.weka » wekaPython
Integration with CPython for Weka. Python version 2.7.x or higher is required. Also requires the following packages to be installed in python: numpy, pandas, matplotlib and scikit-learn. This package provides a wrapper classifier and clusterer that, between them, cover 60+ scikit-learn algorithms. It also provides a general scripting step for the Knowlege Flow along with scripting plugin environments for the Explorer and Knowledge Flow.
更新时间: 2022-11-25 12:06nz.ac.waikato.cms.weka » tiny-weka
The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This artifact represents the bare API of the developer version, with no package manager, PMML, XML or user interface. It is aimed at commercial applications that license some of WEKA's algorithms.
更新时间: 2022-03-04 05:01nz.ac.waikato.cms.weka » weka-dev
The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
更新时间: 2022-01-28 07:19nz.ac.waikato.cms.weka » weka-stable
The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
更新时间: 2022-01-28 07:16nz.ac.waikato.cms.weka » hotSpot
HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.
更新时间: 2021-08-10 07:17nz.ac.waikato.cms.weka » optics_dbScan
The OPTICS and DBScan clustering algorithms. Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996; Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD International Conference on Management of Data, 49-60, 1999.
更新时间: 2019-12-16 09:29nz.ac.waikato.cms.weka » elasticNet
An implementation of the elastic net method for linear regression
更新时间: 2019-12-02 11:40nz.ac.waikato.cms.weka » javaFXScatter3D
A visualization component for displaying a 3D scatter plot of the data using Java 3D. Requires Java 3D to be installed. This version adds built-in sampling controls to the GUI. The default sampling percentage is set so that a maximum of 5000 instances are plotted. The user can adjust this higher or lower to suit their available processing speed and memory.
更新时间: 2019-10-31 09:32nz.ac.waikato.cms.weka » timeseriesForecasting
Provides a time series forecasting environment for Weka. Includes a wrapper for Weka regression schemes that automates the process of creating lagged variables and date-derived periodic variables and provides the ability to do closed-loop forecasting. New evaluation routines are provided by a special evaluation module and graphing of predictions/forecasts are provided via the JFreeChart library. Includes both command-line and GUI user interfaces. Sample time series data can be found in ${WEKA_HOME}/packages/timeseriesForecasting/sample-data.
更新时间: 2019-10-25 06:30nz.ac.waikato.cms.weka » scatterPlot3D
A visualization component for displaying a 3D scatter plot of the data using Java 3D. Requires Java 3D to be installed. This version adds built-in sampling controls to the GUI. The default sampling percentage is set so that a maximum of 5000 instances are plotted. The user can adjust this higher or lower to suit their available processing speed and memory.
更新时间: 2019-02-04 10:52nz.ac.waikato.cms.weka » discriminantAnalysis
Currently only contains Fisher's Linear Discriminant Analysis.
更新时间: 2018-10-28 09:32nz.ac.waikato.cms.weka » XMeans
Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.
更新时间: 2018-02-28 10:23nz.ac.waikato.cms.weka » distributedWekaBase
This package provides generic configuration class and distributed map/reduce style tasks for Weka
更新时间: 2018-02-27 09:22nz.ac.waikato.cms.weka » partialLeastSquares
This package contains a filter for computing partial least squares and transforming the input data into the PLS space. It also contains a classifier for performing PLS regression.
更新时间: 2018-01-11 10:33nz.ac.waikato.cms.weka » WekaExcel
WekaExcel adds support to directory read from and write to spreadsheets in Microsoft Excel 97-2007 format. It uses Apache POI (http://poi.apache.org/), specifically POI-HSSF and POI-XSSF (http://poi.apache.org/spreadsheet/), in order to read/write Excel spreadsheets.
更新时间: 2018-01-08 12:14| 仓库 | 个数 |
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