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Logo Optunity 1.1.1

by claesenm - September 30, 2015, 07:06:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4216 views, 1027 downloads, 3 subscriptions

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.


This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

Logo Chalearn gesture challenge code by jun wan 2.0

by joewan - September 29, 2015, 08:50:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 4145 views, 1040 downloads, 2 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.


Initial Announcement on

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 525 views, 82 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.


Initial Announcement on

Logo python weka wrapper 0.3.3

by fracpete - September 26, 2015, 06:11:42 CET [ Project Homepage BibTeX Download ] 19119 views, 4101 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

  • updated to Weka 3.7.13
  • documentation now covers the API as well

Logo r-cran-arules 1.2-1

by r-cran-robot - September 20, 2015, 00:00:00 CET [ Project Homepage BibTeX Download ] 22740 views, 4798 downloads, 3 subscriptions

About: Mining Association Rules and Frequent Itemsets


Fetched by r-cran-robot on 2015-11-01 00:00:03.930344

Logo KEEL Knowledge Extraction based on Evolutionary Learning 3.0

by keel - September 18, 2015, 12:38:54 CET [ Project Homepage BibTeX Download ] 603 views, 181 downloads, 1 subscription

About: KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a two-fold goal: research and educational. KEEL is also coupled with KEEL-dataset: a webpage that aims at providing to the machine learning researchers a set of benchmarks to analyze the behavior of the learning methods. Concretely, it is possible to find benchmarks already formatted in KEEL format for classification (such as standard, multi instance or imbalanced data), semi-supervised classification, regression, time series and unsupervised learning. Also, a set of low quality data benchmarks is maintained in the repository.


Initial Announcement on

Logo ELKI 0.7.0-20150828

by erich - September 17, 2015, 10:20:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15550 views, 2841 downloads, 4 subscriptions

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.


Additions and Improvements from ELKI 0.6.0:

  • 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. 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

Clustering algorithms:


  • speedups for some initialization heuristics
  • K-means++ initialization no longer squares distances (again)
  • farthest-point heuristics now uses minimum instead of sum (renamed)
  • additional evaluation criteria
  • Elkan's and Hamerly's faster k-means variants

CLARA clustering


Hierarchical clustering

  • Renamed naive algorithm to AGNES
  • Anderbergs algorithm (faster than AGNES, slower than SLINK)
  • CLINK for complete linkage clustering in O(n²) time, O(n) memory
  • Simple extraction from HDBSCAN
  • "Optimal" extraction from HDBSCAN
  • HDBSCAN, in two variants

LSDBC clustering

EM clustering was refactored and moved into its own package. The new version is much more extensible.

Parallel computation framework, and some parallelized algorithms

  • Parallel k-means
  • Parallel LOF and variants


  • LibSVM format parser


  • kNN classification (with index acceleration)

Evaluation: Internal cluster evaluation:

  • Silhouette index
  • Simplified Silhouette index (faster)
  • Davis-Bouldin index
  • PBM index
  • Variance-Ratio-Criteria
  • Sum of squared errors
  • C-Index
  • Concordant pair indexes (Gamma, Tau)
  • Different noise handling strategies for internal indexes

Statistical dependence measures:

  • Distance correlation dCor.
  • Hoeffings D.
  • Some divergence / mutual information measures.

Distance functions:

  • Big refactoring.
  • Time series distances refactored, allow variable length series now.
  • Hellinger distance and kernel function.


  • Faster MDS implementation using power iterations.

Indexing improvements:

  • Precomputed distance matrix "index".
  • iDistance index (static only).
  • Inverted-list index for sparse data and cosine/arccosine distance.
  • cover tree index (static only).

Frequent Itemset Mining:

  • Improved APRIORI implementation.
  • FP-Growth added.
  • Eclat (basic version only) added.

Uncertain clustering:

  • Discrete and continuous data models
  • FDBSCAN clustering
  • UKMeans clustering
  • CKMeans clustering
  • Representative Uncertain Clustering (Meta-algorithm)
  • Center-of-mass meta Clustering (allows using other clustering algorithms on uncertain objects) (KDD'14)

Outlier detection changes / smaller improvements:

  • KDEOS outlier detection (SDM14)
  • k-means based outlier detection (distance to centroid) and Silhouette coefficient based approach (which does not work too well on the toy data sets - the lowest silhouette are usually where two clusters touch).
  • bug fix in kNN weight, when distances are tied and kNN yields more than k results.
  • kNN and kNN weight outlier have their k parameter changed: old 2NN outlier is now 1NN outlier, as commonly understood in classification literature (1 nearest neighbor ''other than the query object''; whereas in database literature the 1NN is usually the query object itself). You can get the old result back by decreasing k by one easily.
  • LOCI implementation is now only O(n^3 log n) instead of O(n^4).


  • 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).

Logo WEKA 3.7.13

by mhall - September 11, 2015, 04:55:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 51274 views, 7618 downloads, 4 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]


In core weka:

  • Numerically stable implementation of variance calculation in core Weka classes - thanks to Benjamin Weber
  • Unified expression parsing framework (with compiled expressions) is now employed by filters and tools that use mathematical/logical expressions - thanks to Benjamin Weber
  • Developers can now specify GUI and command-line options for their Weka schemes via a new unified annotation-based mechanism
  • ClassConditionalProbabilities filter - replaces the value of a nominal attribute in a given instance with its probability given each of the possible class values
  • GUI package manager's available list now shows both packages that are not currently installed, and those installed packages for which there is a more recent version available that is compatible with the base version of Weka being used
  • ReplaceWithMissingValue filter - allows values to be randomly (with a user-specified probability) replaced with missing values. Useful for experimenting with methods for imputing missing values
  • WrapperSubsetEval can now use plugin evaluation metrics

In packages:

  • alternatingModelTrees package - alternating trees for regression
  • timeSeriesFilters package, contributed by Benjamin Weber
  • distributedWekaSpark package - wrapper for distributed Weka on Spark
  • wekaPython package - execution of CPython scripts and wrapper classifier/clusterer for Scikit Learn schemes
  • MLRClassifier in RPlugin now provides access to almost all classification and regression learners in MLR 2.4

Logo JMLR Darwin 1.9

by sgould - September 8, 2015, 06:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41732 views, 8666 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.


Version 1.9:

  • Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
  • Added function to read CIFAR-10 and CIFAR-100 style datasets (see
  • Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
  • drwnPatchMatchGraph now allows multiple matches to the same image
  • Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
  • Switched Mac OS X compilation to libc++ instead of libstdc++
  • Added Python scripts for running experiments and regression tests
  • Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
  • Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
  • Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
  • Improved documentation, other bug fixes and performance improvements

About: Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU, etc. The toolbox is a user-friendly open source software and is freely available on the website.


New in toolbox

  • Bug was fixed for computeBatchSize function in Linux.
  • Revision of some demo scripts. cardinal

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