About: Misc Functions of the Department of Statistics, Probability Theory Group (FormerlyChanges:
Fetched by r-cran-robot on 2015-12-01 00:00:06.355374
About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.Changes:
Additions and Improvements from ELKI 0.6.0:
ELKI is now available on Maven: https://search.maven.org/#artifactdetails|de.lmu.ifi.dbs.elki|elki|0.7.0|jar
Please clone https://github.com/elki-project/example-elki-project for a minimal project example.
Uncertain data types, and clustering algorithms for uncertain data.
Major refactoring of distances - removal of Distance values and removed support for non-double-valued distance functions (in particular DoubleDistance was removed). While this reduces the generality of ELKI, we could remove about 2.5% of the codebase by not having to have optimized codepaths for double-distance anymore. Generics for distances were present in almost any distance-based algorithm, and we were also happy to reduce the use of generics this way. Support for non-double-valued distances can trivially be added again, e.g. by adding the specialization one level higher: at the query instead of the distance level, for example. In this process, we also removed the Generics from NumberVector. The object-based get was deprecated for a good reason long ago, and e.g. doubleValue are more efficient (even for non-DoubleVectors).
Dropped some long-deprecated classes.
EM clustering was refactored and moved into its own package. The new version is much more extensible.
Parallel computation framework, and some parallelized algorithms
LibSVM format parser.
kNN classification (with index acceleration).
Internal cluster evaluation:
Statistical dependence measures:
Frequent Itemset Mining:
MiniGUI has two "secret" new options: -minigui.last -minigui.autorun to load the last saved configuration and run it, for convenience.
Logging API has been extended, to make logging more convenient in a number of places (saving some lines for progress logging and timing).
About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate classifiers without writing a single line of code.Changes:
This is a major release that includes brand new features as well as a renewed architecture of the entire project.
Now KeLP is organized in four maven projects:
Furthermore this new release includes:
Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.0.0!
About: Software for Automatic Construction and Inference of DBNs Based on Mathematical ModelsChanges:
Initial Announcement on mloss.org.
About: Easily prototype WEKA classifiers and filters using Python scripts.Changes:
About: Classification and Regression TrainingChanges:
Fetched by r-cran-robot on 2015-12-01 00:00:05.446562
About: An open-source Python toolbox to analyze mobile phone metadata.Changes:
Initial Announcement on mloss.org.