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Logo KeLP 1.2.1

by kelpadmin - July 24, 2015, 15:43:13 CET [ Project Homepage BibTeX Download ] 2147 views, 518 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 classifiers without writing a single line of code.

Changes:

The code for learning relations between pairs of short texts has been released, and includes the approach described in:

Simone Filice, Giovanni Da San Martino and Alessandro Moschitti. Relational Information for Learning from Structured Text Pairs. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015.

In particular this new release includes:

  • TreePairRelTagger: a manipulator that establishes relations between two tree representations (available in the maven project discreterepresentation)

  • 5 new kernels on pairs: released in the maven project standard-kernel

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 1.2.1!


About: Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 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. 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, etc. The toolbox is a user-friendly open source software and is freely available on the website.

Changes:

New features

  • GPU support (about 5 times faster than CPU - test in GPU: NVIDEA GeForce GTX 780 CPU: AMD FX 8150 Eight-Core 3.6 GHz)
  • Cast DBN parameters to single and double data types
  • Sparsity in RBM with three different methods
  • Plotting bases function
  • Classification and feature extraction on 20 Newsgroups datasets
  • Code correction in using back propagation.
  • Runtime and memory code optimization in Normalization and Shuffling

cardinal


Logo Optunity 1.1.0

by claesenm - July 19, 2015, 12:23:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2337 views, 610 downloads, 2 subscriptions

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.

Changes:

The following features have been added:

  • new solvers
  • tree of Parzen estimators (requires Hyperopt)
  • Sobol sequences
  • Octave wrapper
  • support for structured search spaces, which can be nested
  • improved cross-validation routines to return more detailed results
  • most Python examples are now available as notebooks

Logo DiffSharp 0.6.3

by gbaydin - July 18, 2015, 22:04:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1831 views, 343 downloads, 3 subscriptions

About: DiffSharp is an automatic differentiation (AD) library providing gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products. It allows exact and efficient calculation of derivatives, with support for nesting.

Changes:

Fixed: Bug fix in DiffSharp.AD subtraction operation between D and DF


Logo JMLR libDAI 0.3.2

by jorism - July 17, 2015, 15:59:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40342 views, 7482 downloads, 4 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.


Logo JMLR GPstuff 4.6

by avehtari - July 15, 2015, 15:08:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22000 views, 5277 downloads, 2 subscriptions

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2015-07-09 Version 4.6

Development and release branches available at https://github.com/gpstuff-dev/gpstuff

New features

  • Use Pareto smoothed importance sampling (Vehtari & Gelman, 2015) for

  • importance sampling leave-one-out cross-validation (gpmc_loopred.m)

  • importance sampling integration over hyperparameters (gp_ia.m)

  • importance sampling part of the logistic Gaussian process density estimation (lgpdens.m)

  • references:

    • Aki Vehtari and Andrew Gelman (2015). Pareto smoothed importance sampling. arXiv preprint arXiv:1507.02646.
    • Aki Vehtari, Andrew Gelman and Jonah Gabry (2015). Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models.
  • New covariance functions

    • gpcf_additive creates a mixture over products of kernels for each dimension reference: Duvenaud, D. K., Nickisch, H., & Rasmussen, C. E. (2011). Additive Gaussian processes. In Advances in neural information processing systems, pp. 226-234.
    • gpcf_linearLogistic corresponds to logistic mean function
    • gpcf_linearMichelismenten correpsonds Michelis Menten mean function

Improvements - faster EP moment calculation for lik_logit

Several minor bugfixes


Logo NaN toolbox 2.8.1

by schloegl - July 6, 2015, 22:43:23 CET [ Project Homepage BibTeX Download ] 35904 views, 7321 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.2.8.1 - number of bug fixes - compatibility issues with recent versions of Octave are addressed - upgrade to libsvm 3-12

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


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.6

by hn - July 6, 2015, 12:31:28 CET [ Project Homepage BibTeX Download ] 25806 views, 5979 downloads, 4 subscriptions

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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Changes:
  • added a new inference function infGrid_Laplace allowing to use non-Gaussian likelihoods for large grids

  • fixed a bug due to Octave evaluating norm([]) to a tiny nonzero value, modified all lik/lik*.m functions reported by Philipp Richter

  • small bugfixes in covGrid and infGrid

  • bugfix in predictive variance of likNegBinom due to Seth Flaxman

  • bugfix in infFITC_Laplace as suggested by Wu Lin

  • bugfix in covPP{iso,ard}


Logo r-cran-CoxBoost 1.4

by r-cran-robot - July 1, 2015, 00:00:05 CET [ Project Homepage BibTeX Download ] 20956 views, 4199 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks

Changes:

Fetched by r-cran-robot on 2015-07-01 00:00:05.138021


Logo r-cran-Boruta 4.0.0

by r-cran-robot - July 1, 2015, 00:00:04 CET [ Project Homepage BibTeX Download ] 11408 views, 2378 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2015-07-01 00:00:04.579991


Showing Items 1-10 of 589 on page 1 of 59: 1 2 3 4 5 6 Next Last