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Logo OpenNN 3.0

by Sergiointelnics - February 11, 2016, 16:55:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4695 views, 829 downloads, 4 subscriptions

About: OpenNN is an open source class library written in C++ programming language which implements neural networks, a main area of deep learning research. The library has been designed to learn from both data sets and mathematical models.

Changes:

New algorithms, correction of bugs, model selection algorithms.


Logo Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18041 views, 3884 downloads, 3 subscriptions

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.

Changes:

Version 1.1.2d (12/29/2015):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!

Logo r-cran-bnclassify 0.3.2

by r-cran-robot - February 1, 2016, 00:00:04 CET [ Project Homepage BibTeX Download ] 529 views, 130 downloads, 2 subscriptions

About: Learning Discrete Bayesian Network Classifiers from Data

Changes:

Fetched by r-cran-robot on 2016-02-01 00:00:04.530516


Logo python weka wrapper 0.3.5

by fracpete - January 29, 2016, 05:22:58 CET [ Project Homepage BibTeX Download ] 22537 views, 4698 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.

Changes:
  • added support for weka.core.BatchPredictor to class Classifier in module weka.classifiers
  • upgraded Weka to revision 12410 (post 3.7.13) to avoid performance bottleneck when using setOptions method
  • fixed class SetupGenerator from module weka.core.classes
  • added load_any_file method to the weka.core.converters module
  • added save_any_file method to the weka.core.converters module
  • if GridSearch instantiation (module weka.classifiers) fails, it now outputs message whether package installed and JVM with package support started

Logo Armadillo library 6.500

by cu24gjf - January 27, 2016, 12:11:29 CET [ Project Homepage BibTeX Download ] 74709 views, 15200 downloads, 5 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use, with a function syntax similar to MATLAB. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, OpenBLAS).

Changes:
  • added stand-alone kmeans() function for clustering data
  • added trunc(), ind2sub() and sub2ind()
  • added conv2() for 2D convolution
  • extended conv() to optionally provide central convolution
  • expanded each_col(), each_row() and each_slice() to handle C++11 lambda functions
  • faster handling of multiply-and-accumulate by accu() when using Intel MKL, ATLAS or OpenBLAS
  • fixes for corner cases in gmm_diag class

Logo libcluster 2.2

by dsteinberg - January 24, 2016, 03:32:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2441 views, 535 downloads, 2 subscriptions

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Changes:

Python 2 & 3 interface fixes and minor updates. Now uses Travis-CI as well.


Logo APCluster 1.4.2

by UBod - January 19, 2016, 13:56:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 28224 views, 4955 downloads, 3 subscriptions

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About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes:
  • switched sequence kernel example in vignette from 'kernlab' to 'kebabs' package
  • workaround to ensure that all apcluster*() methods are able to process KernelMatrix objects (cf. kebabs package)
  • replaced data set ch22Promoters by plain text file (FASTA format) in inst/examples
  • bug fix in the heatmap() method
  • vignette engine changed from Sweave to knitr

About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 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. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. 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 based, etc. The toolbox is a user-friendly open source software in MATLAB and Octave and is freely available on the website.

Changes:

New in toolbox

  • Bug fix in changing learning rate.
  • Expanded generateData function in using after backpropagation.
  • Expanded reconstructData function in using after backpropagation.

cardinal


Logo pattern recognition tool 1.0

by openpr_nlpr - January 19, 2016, 03:54:11 CET [ Project Homepage BibTeX Download ] 408 views, 139 downloads, 3 subscriptions

About: a tool for marking samples in images for database building, also including algorithm of LBP,HOG,and classifiers of SVM (six kernels), adaboost,BP and convolutional networks, extreme learning machine.

Changes:

Initial Announcement on mloss.org.


Logo KeLP 2.0.1

by kelpadmin - January 13, 2016, 12:47:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5768 views, 1433 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:

  • Soft Confidence Weighted Classification algorithm: a brand new online learning algorithm from Wang, J., Zhao, P., Hoi, S.C.: Exact soft confidence-weighted learning. In Proceedings of the ICML 2012. ACM, New York, NY, USA (2012)

  • Optimization of the kernel caching mechanism

  • The Smooth Partial Tree Kernel and the Partial Tree Kernel now have the possibility to specify a maximum branching factor (parameter: maxSubseqLeng) in the tree fragments considered by the kernel operation.

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 2.0.1!


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