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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 ] 26058 views, 5568 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.

Changes:

See http://dl-learner.org/development/changelog/.


Logo DRVQ 1.0.1-beta

by iavr - January 18, 2014, 17:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3703 views, 779 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.

Changes:

Initial Announcement on mloss.org.


Logo DynaML 1.4.1

by mandar2812 - April 20, 2017, 18:32:33 CET [ Project Homepage BibTeX Download ] 816 views, 174 downloads, 1 subscription

About: DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes implementing predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows.

Changes:

Initial Announcement on mloss.org.


Logo dysii Dynamic Systems Library 1.4.0

by lawmurray - December 17, 2008, 17:33:41 CET [ Project Homepage BibTeX Download ] 7418 views, 1941 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 [...]

Changes:

Initial Announcement on mloss.org.


Logo EANT Without Structural Optimization 1.0

by yk - September 28, 2009, 12:34:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6690 views, 2048 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.

Changes:

Initial Announcement on mloss.org.


Logo Easysvm 0.3

by gxr - June 25, 2009, 18:33:04 CET [ Project Homepage BibTeX Download ] 11170 views, 2264 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.

Changes:

Fixes for shogun 0.7.3.


Logo Eblearn pre-release

by cpoulet - October 10, 2008, 22:20:23 CET [ Project Homepage BibTeX Download ] 5657 views, 1333 downloads, 0 comments, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


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 ] 3873 views, 764 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


About: This MATLAB package provides the MLAPG algorithm proposed in our ICCV 2015 paper. It is efficient for PSD constrained metric learning, and also effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/mlapg/.

Changes:

Initial Announcement on mloss.org.


Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 24698 views, 8965 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 [...]

Changes:

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.


Showing Items 121-130 of 648 on page 13 of 65: First Previous 8 9 10 11 12 13 14 15 16 17 18 Next Last