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Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 22319 views, 8543 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.


Logo Local high order regularization 1.0

by kkim - March 2, 2016, 13:46:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 908 views, 227 downloads, 2 subscriptions

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About: Local high-order regularization for semi-supervised learning

Changes:

Initial Announcement on mloss.org.


Logo lomo feature extraction and xqda metric learning for person reidentification 1.0

by openpr_nlpr - May 6, 2015, 11:38:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2475 views, 372 downloads, 3 subscriptions

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About: This MATLAB package provides the LOMO feature extraction and the XQDA metric learning algorithms proposed in our CVPR 2015 paper. It is fast, and effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/.

Changes:

Initial Announcement on mloss.org.


Logo slim for matlab 0.2

by ustunb - August 23, 2016, 20:27:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 179 views, 16 downloads, 2 subscriptions

About: learn optimized scoring systems using MATLAB and the CPLEX Optimization Studio

Changes:

Initial Announcement on mloss.org.


Logo NaN toolbox 3.0.3

by schloegl - August 19, 2016, 11:08:57 CET [ Project Homepage BibTeX Download ] 48182 views, 9779 downloads, 3 subscriptions

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes:

Changes in v.3.0.3 - improve compatibility for Octave on Windows

Changes in v.3.0.1 - fix packaging for octave

Changes in v.2.8.5 - bug fix: trimmean - compiler support for gcc-5 and clang - fix typos

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG


Logo JMLR dlib ml 19.1

by davis685 - August 13, 2016, 20:24:13 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 148676 views, 24001 downloads, 5 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds support for cuDNN 5.1 as well as a number of minor bug fixes and usability improvements.


Logo KeLP 2.1.0

by kelpadmin - August 11, 2016, 10:40:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9364 views, 2274 downloads, 3 subscriptions

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 prediction models without writing a single line of code.

Changes:

In addition to minor bug fixes, this release includes:

  • a flexible system to manipulate example-pairs
  • new manipulators for performing tree pruning
  • new examples for the usage of kelp

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 2.1.0!


Logo Somoclu 1.6.2

by peterwittek - August 9, 2016, 14:30:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16973 views, 3264 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, R, and MATLAB are supported.

Changes:
  • Changed: In-place codebook updates when compiled without MPI. This improves update speed and substantially cuts memory use.
  • Changed: Compatible with Visual Studio 15.
  • Fixed: The BMUs returned after training were from before the last epoch. Now another round of BMU search is done.
  • Fixed: Training can continue on the same data in the Python wrapper.
  • Fixed: GPU memory allocation problem on Windows.

Logo AMIDST Toolbox 0.5.1

by ana - August 8, 2016, 13:45:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2759 views, 388 downloads, 4 subscriptions

About: A Java Toolbox for Scalable Probabilistic Machine Learning.

Changes:

The new added functionalities include support to Flink for distributed learning of probabilistic graphical models and support for Latent Dirichlet Allocation Models for text analysis purposes.

Detailed information can be found in the toolbox's web page


Logo revrand 0.6.0

by dsteinberg - August 8, 2016, 08:39:08 CET [ Project Homepage BibTeX Download ] 4731 views, 872 downloads, 3 subscriptions

About: A library of scalable Bayesian generalised linear models with fancy features

Changes:
  • The GLM now uses Auto-encoding variational Bayes for inference as opposed to nonparametric variational inference. This substantially improves performance and simplifies the codebase.
  • Many bugfixes.

Showing Items 61-70 of 623 on page 7 of 63: First Previous 2 3 4 5 6 7 8 9 10 11 12 Next Last