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Logo JMLR DLLearner 1.0

by Jens - February 13, 2015, 11:39:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18444 views, 4461 downloads, 6 subscriptions

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About: The DL-Learner framework contains several algorithms for supervised concept learning in Description Logics (DLs) and OWL.



Logo DRVQ 1.0.1-beta

by iavr - January 18, 2014, 17:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2083 views, 490 downloads, 1 subscription

About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast.


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Logo dysii Dynamic Systems Library 1.4.0

by lawmurray - December 17, 2008, 17:33:41 CET [ Project Homepage BibTeX Download ] 6125 views, 1577 downloads, 0 subscriptions

About: dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and [...]


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Logo EANT Without Structural Optimization 1.0

by yk - September 28, 2009, 12:34:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5360 views, 1774 downloads, 1 subscription

About: EANT Without Structural Optimization is used to learn a policy in either complete or partially observable reinforcement learning domains of continuous state and action space.


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Logo Easysvm 0.3

by gxr - June 25, 2009, 18:33:04 CET [ Project Homepage BibTeX Download ] 9212 views, 1827 downloads, 1 subscription

About: The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way.


Fixes for shogun 0.7.3.

Logo Eblearn pre-release

by cpoulet - October 10, 2008, 22:20:23 CET [ Project Homepage BibTeX Download ] 4685 views, 1120 downloads, 0 comments, 1 subscription

About: Eblearn is an object-oriented C++ library that implements various


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Logo Efficient Nonnegative Sparse Coding Algorithm 1.0

by openpr_nlpr - January 4, 2012, 09:44:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2737 views, 565 downloads, 1 subscription

About: Nonnegative Sparse Coding, Discriminative Semi-supervised Learning, sparse probability graph


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Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 19594 views, 8071 downloads, 2 subscriptions

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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]


This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.

Logo ELF Ensemble Learning Framework 0.1

by mjahrer - May 10, 2010, 23:54:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5884 views, 1018 downloads, 1 subscription

About: ELF provides many well implemented supervised learners for classification and regression tasks with an opportunity of ensemble learning.


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Logo ELKI 0.7.0-20150828

by erich - September 17, 2015, 10:20:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15567 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).

Showing Items 111-120 of 604 on page 12 of 61: First Previous 7 8 9 10 11 12 13 14 15 16 17 Next Last