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About: Matlab code for semi-supervised regression and dimensionality reduction using Hessian energy.

Changes:

Initial Announcement on mloss.org.


Logo Java Optimized Processor for Embedded Machine Learning 1

by rasped - December 15, 2009, 12:51:26 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4341 views, 864 downloads, 1 subscription

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About: JOP is a Java virtual machine implemented in hardware. It is a hard real-time open source multicore processor capable of worst case execution time analysis of Java code.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-ROCR 1.0-4

by r-cran-robot - December 8, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 5099 views, 1073 downloads, 1 subscription

About: Visualizing the performance of scoring classifiers.

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:08.344833


Logo SCD 2.1

by ambujtewari - December 3, 2009, 22:21:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6856 views, 1317 downloads, 1 subscription

About: A (randomized) coordinate descent procedure to minimize L1 regularized loss for classification and regression purposes.

Changes:

Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.


About: A Java library to create, process and manage mixtures of exponential families.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-RPMM 1.06

by r-cran-robot - November 16, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 1033 views, 214 downloads, 0 subscriptions

About: Recursively Partitioned Mixture Model

Changes:

Initial Announcement on mloss.org by r-cran-robot


Logo kernlab 0.9-9

by alexis - November 2, 2009, 16:03:50 CET [ Project Homepage BibTeX Download ] 10341 views, 2129 downloads, 0 subscriptions

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About: kernlab provides kernel-based Machine Learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab [...]

Changes:

minor fixes in kcca and ksvm functions


Logo seqan 1.2

by sonne - November 2, 2009, 14:54:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6530 views, 1256 downloads, 1 subscription

About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data.

Changes:
  • 5 more applications, i.e. DFI, MicroRazerS, PairAlign, SeqCons, TreeRecon
  • stable release of RazerS supporting paired-end read mapping and configurable sensitivity
  • new alignment algorithms, e.g. banded, configurable alignments (overlap, semi-global, ...)
  • realignment algorithm
  • NGS data structures and formats, e.g. SAM, Amos, ...
  • new alphabets, e.g. Dna with base call qualities, profile characters
  • auxiliary data structures and algorithms, e.g. double ended queue, command line parser
  • positional scores
  • CMake support

Logo JMLR Shark 2.3.0

by igel - October 24, 2009, 22:12:48 CET [ Project Homepage BibTeX Download ] 26757 views, 5426 downloads, 1 subscription

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About: SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides various machine learning and computational intelligence techniques.

Changes:
  • new build system
  • minor bug fixes

Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 17841 views, 7598 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.


Showing Items 421-430 of 565 on page 43 of 57: First Previous 38 39 40 41 42 43 44 45 46 47 48 Next Last