All entries.
Showing Items 1-10 of 590 on page 1 of 59: 1 2 3 4 5 6 Next Last

Logo r-cran-CoxBoost 1.4

by r-cran-robot - August 1, 2015, 00:00:05 CET [ Project Homepage BibTeX Download ] 21075 views, 4213 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 2015-08-01 00:00:05.602281


Logo r-cran-e1071 1.6-6

by r-cran-robot - August 1, 2015, 00:00:05 CET [ Project Homepage BibTeX Download ] 17125 views, 3619 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 1 vote)

About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly

Changes:

Fetched by r-cran-robot on 2015-08-01 00:00:05.730750


Logo r-cran-Boruta 4.0.0

by r-cran-robot - August 1, 2015, 00:00:04 CET [ Project Homepage BibTeX Download ] 11551 views, 2391 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2015-08-01 00:00:04.616371


Logo Universal Java Matrix Package 0.3.0

by arndt - July 31, 2015, 14:23:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11118 views, 2075 downloads, 1 subscription

About: The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multi-threading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more.

Changes:

Updated to version 0.3.0


Logo RiVal 0.1

by alansaid - July 29, 2015, 12:39:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 113 views, 13 downloads, 2 subscriptions

About: Rival is an open source Java toolkit for recommender system evaluation. It provides a simple way to create evaluation results comparable across different recommendation frameworks.

Changes:

Initial Announcement on mloss.org.


Logo KeLP 1.2.1

by kelpadmin - July 24, 2015, 15:43:13 CET [ Project Homepage BibTeX Download ] 2265 views, 552 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 ] 2451 views, 627 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 ] 1928 views, 369 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 ] 40541 views, 7524 downloads, 4 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

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.


Showing Items 1-10 of 590 on page 1 of 59: 1 2 3 4 5 6 Next Last