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Logo Theano 0.7

by jaberg - March 27, 2015, 16:40:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15042 views, 2724 downloads, 1 subscription

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.7 (26th of March, 2015)

We recommend to everyone to upgrade to this version.

Highlights:

* Integration of CuDNN for 2D convolutions and pooling on supported GPUs
* Too many optimizations and new features to count
* Various fixes and improvements to scan
* Better support for GPU on Windows
* On Mac OS X, clang is used by default
* Many crash fixes
* Some bug fixes as well

Logo JMLR dlib ml 18.14

by davis685 - March 1, 2015, 23:51:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 94418 views, 16424 downloads, 3 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 an implementation of spectral clustering as well as a few bug fixes and usability improvements.


Logo CN24 Convolutional Neural Networks for Semantic Segmentation 1.0

by erik - February 23, 2015, 09:02:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 576 views, 76 downloads, 1 subscription

About: CN24 is a complete semantic segmentation framework using fully convolutional networks.

Changes:

Initial Announcement on mloss.org.


Logo JMLR SHOGUN 4.0.0

by sonne - February 5, 2015, 09:09:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 91151 views, 12723 downloads, 6 subscriptions

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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.

Changes:

This release features the work of our 8 GSoC 2014 students [student; mentors]:

  • OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
  • Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
  • Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
  • Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
  • Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
  • Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
  • Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
  • Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]

It also contains several cleanups and bugfixes:

Features

  • New Shogun project description [Heiko Strathmann]
  • ID3 algorithm for decision tree learning [Parijat Mazumdar]
  • New modes for PCA matrix factorizations: SVD & EVD, in-place or reallocating [Parijat Mazumdar]
  • Add Neural Networks with linear, logistic and softmax neurons [Khaled Nasr]
  • Add kernel multiclass strategy examples in multiclass notebook [Saurabh Mahindre]
  • Add decision trees notebook containing examples for ID3 algorithm [Parijat Mazumdar]
  • Add sudoku recognizer ipython notebook [Alejandro Hernandez]
  • Add in-place subsets on features, labels, and custom kernels [Heiko Strathmann]
  • Add Principal Component Analysis notebook [Abhijeet Kislay]
  • Add Multiple Kernel Learning notebook [Saurabh Mahindre]
  • Add Multi-Label classes to enable Multi-Label classification [Thoralf Klein]
  • Add rectified linear neurons, dropout and max-norm regularization to neural networks [Khaled Nasr]
  • Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]
  • Add support for arbitrary acyclic graph-structured neural networks [Khaled Nasr]
  • Add CART algorithm for classification and regression using decision trees [Parijat Mazumdar]
  • Add CHAID algorithm for multiclass classification and regression using decision trees [Parijat Mazumdar]
  • Add Convolutional Neural Networks [Khaled Nasr]
  • Add Random Forests algorithm for ensemble learning using CART [Parijat Mazumdar]
  • Add Restricted Botlzmann Machines [Khaled Nasr]
  • Add Stochastic Gradient Boosting algorithm for ensemble learning [Parijat Mazumdar]
  • Add Deep contractive and denoising autoencoders [Khaled Nasr]
  • Add Deep belief networks [Khaled Nasr]

Bugfixes

  • Fix reference counting bugs in CList when reference counting is on [Heiko Strathmann, Thoralf Klein, lambday]
  • Fix memory problem in PCA::apply_to_feature_matrix [Parijat Mazumdar]
  • Fix crash in LeastAngleRegression for the case D greater than N [Parijat Mazumdar]
  • Fix memory violations in bundle method solvers [Thoralf Klein]
  • Fix fail in library_mldatahdf5.cpp example when http://mldata.org is not working properly [Parijat Mazumdar]
  • Fix memory leaks in Vowpal Wabbit, LibSVMFile and KernelPCA [Thoralf Klein]
  • Fix memory and control flow issues discovered by Coverity [Thoralf Klein]
  • Fix R modular interface SWIG typemap (Requires SWIG >= 2.0.5) [Matt Huska]

Cleanup and API Changes

  • PCA now depends on Eigen3 instead of LAPACK [Parijat Mazumdar]
  • Removing redundant and fixing implicit imports [Thoralf Klein]
  • Hide many methods from SWIG, reducing compile memory by 500MiB [Heiko Strathmann, Fernando Iglesias, Thoralf Klein]

Logo WEKA 3.7.12

by mhall - December 17, 2014, 03:04:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 44063 views, 6554 downloads, 3 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

In core weka:

  • GUIChooser now has a plugin exension point that allows implementations of GUIChooser.GUIChooserMenuPlugin to appear as entries in either the Tools or Visualization menus
  • SubsetByExpression filter now has support for regexp matching
  • weka.classifiers.IterativeClassifierOptimizer - a classifier that can efficiently optimize the number of iterations for a base classifier that implements IterativeClassifier
  • Speedup for LogitBoost in the two class case
  • weka.filters.supervised.instance.ClassBalancer - a simple filter to balance the weight of classes
  • New class hierarchy for stopwords algorithms. Includes new methods to read custom stopwords from a file and apply multiple stopwords algorithms
  • Ability to turn off capabilities checking in Weka algorithms. Improves runtime for ensemble methods that create a lot of simple base classifiers
  • Memory savings in weka.core.Attribute
  • Improvements in runtime for SimpleKMeans and EM
  • weka.estimators.UnivariateMixtureEstimator - new mixture estimator

In packages:

  • New discriminantAnalysis package. Provides an implementation of Fisher's linear discriminant analysis
  • Quartile estimators, correlation matrix heat map and k-means++ clustering in distributed Weka
  • Support for default settings for GridSearch via a properties file
  • Improvements in scripting with addition of the offical Groovy console (kfGroovy package) from the Groovy project and TigerJython (new tigerjython package) as the Jython console via the GUIChooser
  • Support for the latest version of MLR in the RPlugin package
  • EAR4 package contributed by Vahid Jalali
  • StudentFilters package contributed by Chris Gearhart
  • graphgram package contributed by Johannes Schneider

Logo LogRegCrowds, Logistic Regression from Crowds 1.0

by fmpr - October 30, 2014, 19:10:23 CET [ Project Homepage BibTeX Download ] 593 views, 154 downloads, 2 subscriptions

About: LogReg-Crowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979).

Changes:

Initial Announcement on mloss.org.


About: This library implements the Optimum-Path Forest classifier for unsupervised and supervised learning.

Changes:

Initial Announcement on mloss.org.


Logo hca 0.61

by wbuntine - September 10, 2014, 03:33:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7241 views, 1328 downloads, 4 subscriptions

About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Changes:

Corrections to diagnostics and topic report. Correction to estimating alpha. Now estimating beta sometimes (when estimating phi).


Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26860 views, 7772 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


Logo JMLR MSVMpack 1.5

by lauerfab - July 3, 2014, 16:02:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13729 views, 4527 downloads, 2 subscriptions

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Windows binaries are now included (by Emmanuel Didiot)
  • MSVMpack can now be compiled on Windows (by Emmanuel Didiot)
  • Fixed polynomial kernel
  • Minor bug fixes

Logo FEAST 1.1.1

by apocock - June 30, 2014, 01:30:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20011 views, 4399 downloads, 1 subscription

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About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab.

Changes:
  • Bug fixes to memory management.
  • Compatibility changes for PyFeast python wrapper (note the C library now returns feature indices starting from 0, the Matlab wrapper still returns indices starting from 1).
  • Added C version of MIM.
  • Updated internal version of MIToolbox.

Logo MIToolbox 2.1

by apocock - June 30, 2014, 01:05:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15930 views, 2934 downloads, 1 subscription

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, Renyi entropy/mutual information, and weighted variants of Shannon entropies/mutual informations. Works with discrete distributions, and expects column vectors of features.

Changes:

Added weighted entropy functions. Fixed a few memory handling bugs.


Logo OpenOpt 0.54

by Dmitrey - June 15, 2014, 14:50:37 CET [ Project Homepage BibTeX Download ] 46274 views, 9730 downloads, 3 subscriptions

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About: Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, NSP, MILP, LSP, LLSP, MMP, GLP, SLE, MOP etc; general logical constraints, categorical variables, automatic differentiation, stochastic programming, interval analysis, many other goodies

Changes:

http://openopt.org/Changelog


Logo JMLR Tapkee 1.0

by blackburn - April 10, 2014, 02:45:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7449 views, 2181 downloads, 1 subscription

About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction.

Changes:

Initial Announcement on mloss.org.


Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12699 views, 5049 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26569 views, 4619 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Changes:

compilation problems fixed


Logo GBAC 0.0.4

by henrydcl - November 22, 2013, 20:04:16 CET [ BibTeX BibTeX for corresponding Paper Download ] 3015 views, 963 downloads, 2 subscriptions

About: Probabilistic performance evaluation for multiclass classification using the posterior balanced accuracy

Changes:

Added bibtex information.


Logo JMLR CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15833 views, 5108 downloads, 1 subscription

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


Logo bob 1.2.2

by anjos - October 28, 2013, 14:37:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6242 views, 1385 downloads, 1 subscription

About: Bob is a free signal-processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland.

Changes:

Bob 1.2.0 comes about 1 year after we released Bob 1.0.0. This new release comes with a big set of new features and lots of changes under the hood to make your experiments run even smoother. Some statistics:

Diff URL: https://github.com/idiap/bob/compare/v1.1.4...HEAD Commits: 629 Files changed: 954 Contributors: 7

Here is a quick list of things you should pay attention for while integrating your satellite packages against Bob 1.2.x:

  • The LBP module had its API changed look at the online docs for more details
  • LLRTrainer has been renamed to CGLogRegTrainer
  • The order in which you pass data to CGLogRegTrainer has been inverted (negatives now go first)
  • For C++ bindings, includes are in bob/python instead of bob/core/python
  • All specialized Bob exceptions are gone, if you were catching them, most have been cast into std::runtime_error's

For a detailed list of changes and additions, please look at our Changelog page for this release and minor updates:

https://github.com/idiap/bob/wiki/Changelog-from-1.1.4-to-1.2 https://github.com/idiap/bob/wiki/Changelog-from-1.2.0-to-1.2.1 https://github.com/idiap/bob/wiki/Changelog-from-1.2.1-to-1.2.2


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