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Logo libnabo 1.0.4

by smagnenat - September 4, 2013, 09:34:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6992 views, 1649 downloads, 1 subscription

About: libnabo is a fast K Nearset Neighbor library for low-dimensional spaces.

Changes:
  • Added parameter check for optionFlags in queries.
  • Fixed OS X compilation.

Logo Graph Learning Package 0.1

by hiroto - May 4, 2009, 17:07:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6982 views, 1319 downloads, 0 subscriptions

About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features.

Changes:

Initial Announcement on mloss.org.


Logo MPI IKL 1.0

by pgehler - January 16, 2009, 16:39:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6966 views, 1311 downloads, 1 subscription

About: This package contains an implementation of the Infinite Kernel Learning (IKL) algorithm and the SimpleMKL algorithm. This is realized by building on Coin-Ipopt-3.3.5 and Libsvm.

Changes:

Initial Announcement on mloss.org.


Logo pGBRT, Parallel Gradient Boosted Regression Trees 0.9

by swtyree - September 16, 2011, 22:15:46 CET [ Project Homepage BibTeX Download ] 6936 views, 1071 downloads, 1 subscription

About: Learns gradient boosted regression tree ensembles in parallel on shared memory or cluster systems

Changes:

Initial Announcement on mloss.org.


Logo python weka wrapper 0.2.0

by fracpete - December 22, 2014, 09:21:53 CET [ Project Homepage BibTeX Download ] 6886 views, 1441 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:

NB: This release is not backwards compatible!

  • requires "JavaBridge" 1.0.9 at least
  • moved from Java-like get/set ("getIndex()" and "setIndex(int)") to nicer Python properties
  • using Python properties (also only read-only ones) wherevere possible
  • added "weka.core.version" for accessing the Weka version currently in use
  • added "jwrapper" and "jclasswrapper" methods to "JavaObject" class (the mother of all objects in python-weka-wrapper) to allow generic access to an object's methods: http://pythonhosted.org//javabridge/highlevel.html#wrapping-java-objects-using-reflection
  • added convenience methods "class_is_last()" and "class_is_first()" to "weka.core.Instances" class
  • added convenience methods "delete_last_attribute()" and "delete_first_attribute()" to "weka.core.Instances" class

Logo SMIDAS 1.1

by ambujtewari - August 15, 2010, 18:51:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6837 views, 1366 downloads, 1 subscription

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression.

Changes:

Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.


Logo LibSGDQN 1.1

by antojne - July 2, 2009, 15:02:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6821 views, 1359 downloads, 1 subscription

About: LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs.

Changes:

small bug fix (thx nicolas ;)


Logo r-cran-lasso2 1.2-14

by r-cran-robot - November 20, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 6802 views, 1402 downloads, 1 subscription

About: L1 constrained estimation aka `lasso'

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.967868


Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6785 views, 1246 downloads, 1 subscription

About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16.

Changes:

VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme.

VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).


Logo RLS2 MATLAB Toolbox 0.7

by posaune - March 31, 2010, 20:37:11 CET [ Project Homepage BibTeX Download ] 6738 views, 1418 downloads, 1 subscription

About: RLS2 is an instance of multiple kernel learning algorithm to simultaneously learn a regularized predictor and the kernel function. RLS2LIN is a version of RLS2 specialized to linear kernels on each feature. The package contains a set of scripts that implements RLS2 and RLS2LIN, together with a Graphic User Interface to load data, perform training, validation, and plot results.

Changes:
  • New kernel functions (rbfall, rbfsingle, polyall, polysingle)
  • Improved interface for pre-processing operations
  • The interface now allows to disable bias
  • Fixed bugs in parameter passing (thanks to Andrea Schirru)

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