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Logo SimpleMKL 0.5

by arakotom - June 11, 2008, 00:56:47 CET [ Project Homepage BibTeX Download ] 10651 views, 2824 downloads, 5 subscriptions

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About: Matlab Multiple Kernel Learning toolbox. Features : MKL for SVM Classification, Regression and MultiClass. It needs SVM-KM Toolbox


Initial Announcement on

Logo r-cran-tgp 2.4-3

by r-cran-robot - December 18, 2011, 00:00:00 CET [ Project Homepage BibTeX Download ] 13289 views, 2799 downloads, 1 subscription

About: Bayesian treed Gaussian process models


Fetched by r-cran-robot on 2012-02-01 00:00:11.834310

Logo JMLR Tapkee 1.0

by blackburn - April 10, 2014, 02:45:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9133 views, 2728 downloads, 1 subscription

About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction.


Initial Announcement on

Logo r-cran-randomForest 4.6-7

by r-cran-robot - October 16, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 13555 views, 2726 downloads, 1 subscription

About: Breiman and Cutler's random forests for classification and regression


Fetched by r-cran-robot on 2013-04-01 00:00:07.638240

Logo Orange 2.6

by janez - February 14, 2013, 18:15:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14410 views, 2725 downloads, 1 subscription

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About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]


The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL.

Changed the BibTeX reference to the paper recently published in JMLR MLOSS.

Logo r-cran-Boruta 5.0.0

by r-cran-robot - October 1, 2015, 00:00:04 CET [ Project Homepage BibTeX Download ] 12904 views, 2724 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection


Fetched by r-cran-robot on 2015-10-01 00:00:04.647336

Logo JProGraM 13.2

by ninofreno - February 13, 2013, 20:29:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13357 views, 2711 downloads, 1 subscription

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.


JProGraM 13.2 -- CHANGE LOG

Release date: February 13, 2012

New features: -- Support for Fiedler random graphs/random field models for large-scale networks (ninofreno.graph.fiedler package); -- Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).

Logo GibbsLDA 0.2

by pxhieu - May 9, 2008, 22:18:52 CET [ Project Homepage BibTeX Download ] 6217 views, 2700 downloads, 1 subscription

About: GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for parameter estimation and inference. GibbsLDA++ is fast and is designed to analyze hidden/latent topic [...]


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 ] 14646 views, 2687 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 DAL 1.1

by ryota - February 18, 2014, 19:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15959 views, 2687 downloads, 1 subscription

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/low-rank learning/reconstruction based on the dual augmented Lagrangian method.

  • Supports weighted lasso (dalsqal1.m, dallral1.m)
  • Supports weighted squared loss (dalwl1.m)
  • Bug fixes (group lasso and elastic-net-regularized logistic regression)

Showing Items 91-100 of 598 on page 10 of 60: First Previous 5 6 7 8 9 10 11 12 13 14 15 Next Last