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Logo JMLR GPstuff 4.5

by avehtari - July 22, 2014, 14:03:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18820 views, 4569 downloads, 2 subscriptions

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2014-07-22 Version 4.5

New features

  • Input dependent noise and signal variance.

    • Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Expectation Propagation for Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication. Preprint http://arxiv.org/abs/1404.5443
  • Sparse stochastic variational inference model.

    • Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes for big data. arXiv preprint http://arxiv.org/abs/1309.6835.
  • Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the posterior predictive distribution of the latent variable.

    • Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, 57(6):1317-1339.

    • Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution and Asymmetric Principal Components Analysis. Communications in Statistics - Theory and Methods, 35(6):1123-1140.

Improvements

  • New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
  • Precomputed demo results (including the figures) are now available in the folder tests/realValues.
  • New demos demonstrating new features etc.
    • demo_epinf, demonstrating the input dependent noise and signal variance model
    • demo_svi_regression, demo_svi_classification
    • demo_modelcomparison2, demo_survival_comparison

Several minor bugfixes


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 JMLR Nieme 1.0

by francis - April 2, 2009, 10:57:38 CET [ Project Homepage BibTeX Download ] 18530 views, 2454 downloads, 1 subscription

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About: Nieme is a C++ machine learning library for large-scale classification, regression and ranking. It provides a simple interface available in C++, Python and Java and a user interface for visualization.

Changes:

Released Nieme 1.0


Logo peewit 0.10

by lorenz - May 7, 2014, 16:04:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18528 views, 3675 downloads, 1 subscription

About: peewit provides services for programming, running and result examination of machine learning experiments. It does not include any ML algorithms, has no GUI, and presumes certain uniformity of the experimental layout. But it does not make assumptions on the type of task under study. The current version-number is 0.10.

Changes:

v-cube with side-cubes


Logo Cognitive Foundry 3.4.0

by Baz - April 3, 2015, 08:28:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18502 views, 2997 downloads, 2 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

Changes:
  • General:
    • Now requires Java 1.7 or higher.
    • Improved compatibility with Java 1.8 functions by removing ClonableSerializable requirement from many function-style interfaces.
  • Common Core:
    • Improved iteration speed over sparse MTJ vectors.
    • Added utility methods for more stable log(1+x), exp(1-x), log(1 - exp(x)), and log(1 + exp(x)) to LogMath.
    • Added method for creating a partial permutations to Permutation.
    • Added methods for computing standard deviation to UnivariateStatisticsUtil.
    • Added increment, decrement, and list view methods to Vector and Matrix.
    • Added shorter versions of get and set for Vector and Matrix getElement and setElement.
    • Added aliases of dot for dotProduct in VectorSpace.
    • Added utility methods for divideByNorm2 to VectorUtil.
  • Learning:
    • Added a learner for a Factorization Machine using SGD.
    • Added a iterative reporter for validation set performance.
    • Added new methods to statistical distribution classes to allow for faster sampling without boxing, in batches, or without creating extra memory.
    • Made generics for performance evaluators more permissive.
    • ParameterGradientEvaluator changed to not require input, output, and gradient types to be the same. This allows more sane gradient definitions for scalar functions.
    • Added parameter to enforce a minimum size in a leaf node for decision tree learning. It is configured through the splitting function.
    • Added ability to filter which dimensions to use in the random subspace and variance tree node splitter.
    • Added ReLU, leaky ReLU, and soft plus activation functions for neural networks.
    • Added IntegerDistribution interface for distributions over natural numbers.
    • Added a method to get the mean of a numeric distribution without boxing.
    • Fixed an issue in DefaultDataDistribution that caused the total to be off when a value was set to less than or equal to 0.
    • Added property for rate to GammaDistribution.
    • Added method to get standard deviation from a UnivariateGaussian.
    • Added clone operations for decision tree classes.
    • Fixed issue TukeyKramerConfidence interval computation.
    • Fixed serialization issue with SMO output.

Logo MDP Modular toolkit for Data Processing 3.3

by otizonaizit - October 4, 2012, 15:17:33 CET [ Project Homepage BibTeX Download ] 18288 views, 4685 downloads, 1 subscription

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About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

Changes:

What's new in version 3.3?

  • support sklearn versions up to 0.12
  • cleanly support reload
  • fail gracefully if pp server does not start
  • several bug-fixes and improvements

Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 18122 views, 7664 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 r-cran-arules 1.1-6

by r-cran-robot - December 7, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 17875 views, 3747 downloads, 3 subscriptions

About: Mining Association Rules and Frequent Itemsets

Changes:

Fetched by r-cran-robot on 2015-04-01 00:00:03.661007


Logo JMLR LIBLINEAR 1.32

by biconnect - September 3, 2008, 17:35:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17575 views, 2008 downloads, 2 subscriptions

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About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM

Changes:

Initial Announcement on mloss.org.


Logo MATLAB spectral clustering package 1.1

by wenyenc - February 4, 2010, 01:54:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17439 views, 3208 downloads, 1 subscription

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About: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...]

Changes:
  • Add bib
  • Add code of nystrom without orthogonalization
  • Add accuracy quality measure
  • Add more running scripts

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