All entries.
Showing Items 71-80 of 622 on page 8 of 63: First Previous 3 4 5 6 7 8 9 10 11 12 13 Next Last

Logo Chalearn gesture challenge code by jun wan 2.0

by joewan - September 29, 2015, 08:50:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 5735 views, 1383 downloads, 2 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.

Changes:

Initial Announcement on mloss.org.


Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 1277 views, 255 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Changes:

Initial Announcement on mloss.org.


Logo KEEL Knowledge Extraction based on Evolutionary Learning 3.0

by keel - September 18, 2015, 12:38:54 CET [ Project Homepage BibTeX Download ] 1433 views, 385 downloads, 1 subscription

About: KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a two-fold goal: research and educational. KEEL is also coupled with KEEL-dataset: a webpage that aims at providing to the machine learning researchers a set of benchmarks to analyze the behavior of the learning methods. Concretely, it is possible to find benchmarks already formatted in KEEL format for classification (such as standard, multi instance or imbalanced data), semi-supervised classification, regression, time series and unsupervised learning. Also, a set of low quality data benchmarks is maintained in the repository.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Darwin 1.9

by sgould - September 8, 2015, 06:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 49591 views, 10376 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.

Changes:

Version 1.9:

  • Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
  • Added function to read CIFAR-10 and CIFAR-100 style datasets (see http://www.cs.utoronto.ca/~kriz/cifar.html)
  • Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
  • drwnPatchMatchGraph now allows multiple matches to the same image
  • Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
  • Switched Mac OS X compilation to libc++ instead of libstdc++
  • Added Python scripts for running experiments and regression tests
  • Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
  • Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
  • Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
  • Improved documentation, other bug fixes and performance improvements

Logo r-cran-e1071 1.6-7

by r-cran-robot - June 1, 2016, 00:00:05 CET [ Project Homepage BibTeX Download ] 25669 views, 5589 downloads, 3 subscriptions

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 2016-06-01 00:00:05.764128


Logo YCML 0.2.2

by yconst - August 24, 2015, 20:28:45 CET [ Project Homepage BibTeX Download ] 1406 views, 315 downloads, 3 subscriptions

About: A Machine Learning framework for Objective-C and Swift (OS X / iOS)

Changes:

Initial Announcement on mloss.org.


Logo Java Data Mining Package 0.3.0

by arndt - August 19, 2015, 15:44:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2018 views, 403 downloads, 3 subscriptions

About: A Java library for machine learning and data analytics

Changes:

Initial Announcement on mloss.org.


Logo jLDADMM 1.0

by dqnguyen - August 19, 2015, 12:52:36 CET [ Project Homepage BibTeX Download ] 1406 views, 369 downloads, 2 subscriptions

About: The Java package jLDADMM is released to provide alternative choices for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models.

Changes:

Initial Announcement on mloss.org.


Logo Presage 0.9.1

by Dzmitry_Lahoda - August 18, 2015, 10:13:05 CET [ BibTeX Download ] 936 views, 302 downloads, 3 subscriptions

About: Presage is an intelligent predictive text entry platform.

Changes:

Initial Announcement on mloss.org.


Logo Sparse Compositional Metric Learning v1.1

by bellet - August 16, 2015, 16:41:20 CET [ BibTeX BibTeX for corresponding Paper Download ] 3581 views, 1246 downloads, 2 subscriptions

About: Scalable learning of global, multi-task and local metrics from data

Changes:

Various minor bug fixes and improvements. The basis and triplet generation now fully supports with datasets with very small classes and arbitrary labels (no need to be consecutive or positive). The computational and memory efficiency of the code when data is high dimensional has been largely improved, and we generate a rectangular (smaller) projection matrix when the number of selected basis is smaller than the dimension. K-NN classification with local metrics has been optimized and made significantly less costly in both time and memory.


Showing Items 71-80 of 622 on page 8 of 63: First Previous 3 4 5 6 7 8 9 10 11 12 13 Next Last