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Logo JMLR dlib ml 18.10

by davis685 - August 29, 2014, 02:56:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 81016 views, 14017 downloads, 2 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:

In addition to a number of usability improvements, this release adds an implementation of the recent paper "One Millisecond Face Alignment with an Ensemble of Regression Trees" by Vahid Kazemi and Josephine Sullivan. This includes tools for performing high quality face landmarking as well as tools for training new landmarking models. See the face_landmark_detection_ex.cpp and train_shape_predictor_ex.cpp example programs for an introduction.


Logo JMLR SHOGUN 3.2.0

by sonne - February 17, 2014, 20:31:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 83901 views, 11614 downloads, 5 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 is mostly a bugfix release:

Features

  • Fully support python3 now
  • Add mini-batch k-means [Parijat Mazumdar]
  • Add k-means++ [Parijat Mazumdar]
  • Add sub-sequence string kernel [lambday]

Bugfixes

  • Compile fixes for upcoming swig3.0
  • Speedup for gaussian process' apply()
  • Improve unit / integration test checks
  • libbmrm uninitialized memory reads
  • libocas uninitialized memory reads
  • Octave 3.8 compile fixes [Orion Poplawski]
  • Fix java modular compile error [Bjoern Esser]

Logo r-cran-caret 6.0-35

by r-cran-robot - August 22, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 54052 views, 11438 downloads, 1 subscription

About: Classification and Regression Training

Changes:

Fetched by r-cran-robot on 2014-10-01 00:00:04.246136


Logo JMLR Information Theoretical Estimators 0.60

by szzoli - June 3, 2014, 00:17:33 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 47076 views, 10056 downloads, 2 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

Changes:
  • Quick test on the Tsallis divergence: introduced.

  • Pearson chi square divergence estimation in the exponential family (MLE + analytical formula): added.


Logo Armadillo library 4.450

by cu24gjf - September 21, 2014, 06:47:34 CET [ Project Homepage BibTeX Download ] 43136 views, 9430 downloads, 3 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:
  • faster handling of matrix transposes within compound expressions
  • expanded symmatu()/symmatl() to optionally disable taking the complex conjugate of elements
  • expanded sort_index() to handle complex vectors
  • expanded the gaussian mixture modelling class with functions to generate random samples

Logo MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ] 49266 views, 9353 downloads, 2 subscriptions

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About: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL.

Changes:

New features:

  • LibSvm(): pred_probability() now returns probability estimates; pred_values() added
  • LibLinear(): pred_values() and pred_probability() added
  • dtw_std: squared Euclidean option added
  • LCS for series composed by real values (lcs_real()) added
  • Documentation

Fix:

  • wavelet submodule: cwt(): it returned only real values in morlet and poul
  • IRelief(): remove np. in learn()
  • fix rfe_kfda and rfe_w2 when p=1

Logo OpenOpt 0.54

by Dmitrey - June 15, 2014, 14:50:37 CET [ Project Homepage BibTeX Download ] 41588 views, 8735 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 MyMediaLite 3.10

by zenog - October 8, 2013, 22:29:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41821 views, 7927 downloads, 1 subscription

About: MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.

Changes:

Mostly bug fixes.

For details see: https://github.com/zenogantner/MyMediaLite/blob/master/doc/Changes


Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 16550 views, 7324 downloads, 2 subscriptions

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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]

Changes:

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.


Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23482 views, 6985 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


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