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Logo pycobra regression analysis and ensemble toolkit 0.2.2

by bhargavvader - December 29, 2017, 13:57:46 CET [ Project Homepage BibTeX Download ] 2638 views, 694 downloads, 3 subscriptions

About: pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem.

Changes:

pycobra is further pep8 compliant, has improved tests and more plotting options.


Logo WEKA 3.9.2

by mhall - December 22, 2017, 03:39:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 81688 views, 18734 downloads, 5 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:

This release include a lot of bug fixes and improvements. Some of these are detailed at

http://jira.pentaho.com/projects/DATAMINING/issues/DATAMINING-771

As usual, for a complete list of changes refer to the changelogs.


Logo ADAMS 17.12.0

by fracpete - December 20, 2017, 09:38:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 34617 views, 6192 downloads, 3 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.

Changes:

Some highlights:

  • Code base was moved to Github
  • Nearly 90 new actors, 25 new conversions
  • much improved deeplearning4j module
  • experimental support for Microsoft's CNTK deep learning framework
  • rsync module
  • MEKA webservice module
  • improved support for image annotations
  • improved LaTeX support
  • Websocket support

Logo Operator Discretization Library 0.6

by jonasadl - December 19, 2017, 15:24:08 CET [ Project Homepage BibTeX Download ] 527 views, 154 downloads, 2 subscriptions

About: Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data.

Changes:

Initial Announcement on mloss.org.


Logo Aboleth 0.7

by dsteinberg - December 14, 2017, 02:39:19 CET [ Project Homepage BibTeX Download ] 2627 views, 774 downloads, 3 subscriptions

About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Changes:

Release 0.7.0

  • Update to TensorFlow r1.4.

  • Tutorials in the documentation on:

  • Interfacing with Keras

  • Saving/loading models

  • How to build a variety of regressors with Aboleth

  • New prediction module with some convenience functions, including freezing the weight samples during prediction.

  • Bayesian convolutional layers with accompanying demo.

  • Allow the number of samples drawn from a model to be varied by using placeholders.

  • Generalise the feature embedding layers to work on matrix inputs (instead of just column vectors).

  • Numerous numerical and usability fixes.


Logo sparkcrowd 0.1.5

by enriquegrodrigo - December 13, 2017, 13:13:35 CET [ Project Homepage BibTeX Download ] 1474 views, 435 downloads, 3 subscriptions

About: A Spark package implementing algorithms for learning from crowdsourced big data.

Changes:

Changes: - Minor improvements in code and documentation


Logo Theano 1.0.1

by jaberg - December 7, 2017, 14:14:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41042 views, 6976 downloads, 3 subscriptions

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 1.0.1 (6th of December, 2017)

This is a maintenance release of Theano, version 1.0.1, with no new features, but some important bug fixes.

Highlights (since 1.0.0):

  • Fixed compilation and improved float16 support for topK on GPU

  • NB: topK support on GPU is experimental and may not work for large input sizes on certain GPUs

  • Fixed cuDNN reductions when axes to reduce have size 1

  • Attempted to prevent re-initialization of the GPU in a child process

  • Fixed support for temporary paths with spaces in Theano initialization

  • Spell check pass on the documentation


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 4.1

by hn - November 27, 2017, 19:26:13 CET [ Project Homepage BibTeX Download ] 52018 views, 11487 downloads, 5 subscriptions

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About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.

Changes:

Logdet-estimation functionality for grid-based approximate covariances

  • Lanczos subspace estimation

  • Chebyshef polynomial expansion

More generic infEP functionality

  • dense computations and sparse approximations using the same code

  • covering KL inference as a special cas of EP

New infKL function contributed by Emtiyaz Khan and Wu Lin

  • Conjugate-Computation Variational Inference algorithm

  • much more scalable than previous versions

Time-series covariance functions on the positive real line

  • covW (i-times integrated) Wiener process covariance

  • covOU (i-times integrated) Ornstein-Uhlenbeck process covariance (contributed by Juan Pablo Carbajal)

  • covULL underdamped linear Langevin process covariance (contributed by Robert MacKay)

  • covFBM Fractional Brownian motion covariance

New covariance functions

  • covWarp implements k(w(x),w(z)) where w is a "warping" function

  • covMatern has been extended to also accept non-integer distance parameters


Logo DFLsklearn, Hyperparameters optimization in Scikit Learn 0.1

by vlatorre - November 23, 2017, 13:14:36 CET [ Project Homepage BibTeX Download ] 713 views, 164 downloads, 1 subscription

About: A method to optimize the hyperparameters for machine learning methods implemented in Scikit-learn based on Derivative Free Optimization

Changes:

Initial Announcement on mloss.org.


Logo r-cran-C50 0.1.1

by r-cran-robot - November 20, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 12282 views, 2673 downloads, 0 subscriptions

About: C5.0 Decision Trees and Rule-Based Models

Changes:

Fetched by r-cran-robot on 2018-02-01 00:00:06.080754


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