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Showing Items 11-20 of 645 on page 2 of 65: Previous 1 2 3 4 5 6 7 Next Last

Logo glyph 0.3.2

by mquade - June 1, 2017, 20:51:52 CET [ Project Homepage BibTeX Download ] 710 views, 211 downloads, 3 subscriptions

About: glyph is a python 3 library based on deap providing abstraction layers for symbolic regression problems.

Changes:

Initial Announcement on mloss.org.


Logo SparklingGraph 0.0.7

by riomus - May 22, 2017, 15:29:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6129 views, 1301 downloads, 3 subscriptions

About: Large scale, distributed graph processing made easy.

Changes:

Graph partitioning methods APSP approximation method Performance improvements License change Bug fixes


Logo Kernel Adaptive Filtering Toolbox 2.0

by steven2358 - May 22, 2017, 10:05:33 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10637 views, 1761 downloads, 2 subscriptions

About: A Matlab benchmarking toolbox for online and adaptive regression with kernels.

Changes:
  • Changes in algorithms' Matlab class format
  • New algorithms
  • Minor improvements and bug fixes

Logo DynaML 1.4.1

by mandar2812 - April 20, 2017, 18:32:33 CET [ Project Homepage BibTeX Download ] 760 views, 159 downloads, 1 subscription

About: DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes implementing predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows.

Changes:

Initial Announcement on mloss.org.


Logo pycobra regression analysis and ensemble toolkit 0.1.0

by bhargavvader - April 19, 2017, 15:04:14 CET [ Project Homepage BibTeX Download ] 736 views, 148 downloads, 2 subscriptions

About: pycobra is a python toolkit to help with regression analysis and visualisation. It provides an implementation of the COBRA predictor aggregation algorithm.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-biglasso 1.3-6

by r-cran-robot - April 12, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 1150 views, 243 downloads, 2 subscriptions

About: Extending Lasso Model Fitting to Big Data

Changes:

Fetched by r-cran-robot on 2017-07-01 00:00:02.052094


Logo Theano 0.9.0

by jaberg - April 10, 2017, 20:30:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 33725 views, 5664 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 0.9.0 (20th of March, 2017)

Highlights (since 0.8.0):

* Better Python 3.5 support
* Better numpy 1.12 support
* Conda packages for Mac, Linux and Windows
* Support newer Mac and Windows versions
* More Windows integration:

    * Theano scripts (``theano-cache`` and ``theano-nose``) now works on Windows
    * Better support for Windows end-lines into C codes
    * Support for space in paths on Windows

* Scan improvements:

    * More scan optimizations, with faster compilation and gradient computation
    * Support for checkpoint in scan (trade off between speed and memory usage, useful for long sequences)
    * Fixed broadcast checking in scan

* Graphs improvements:

    * More numerical stability by default for some graphs
    * Better handling of corner cases for theano functions and graph optimizations
    * More graph optimizations with faster compilation and execution
    * smaller and more readable graph

* New GPU back-end:

    * Removed warp-synchronous programming to get good results with newer CUDA drivers
    * More pooling support on GPU when cuDNN isn't available
    * Full support of ignore_border option for pooling
    * Inplace storage for shared variables
    * float16 storage
    * Using PCI bus ID of graphic cards for a better mapping between theano device number and nvidia-smi number
    * Fixed offset error in ``GpuIncSubtensor``

* Less C code compilation
* Added support for bool dtype
* Updated and more complete documentation
* Bug fixes related to merge optimizer and shape inference
* Lot of other bug fixes, crashes fixes and warning improvements

Logo Calibrated AdaMEC 1.0

by nnikolaou - April 8, 2017, 13:57:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1176 views, 188 downloads, 3 subscriptions

About: Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data.

Changes:

Updated license information


Logo KeLP 2.2.0

by kelpadmin - April 7, 2017, 16:51:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16154 views, 3530 downloads, 3 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.

Changes:

In addition to minor bug fixes, this release includes:

  • A new learning algorithm that enable (for the first time in KeLP) to deal with sequences labeling problems! It is based on a Markovian formulation within a SVM framework. Most noticeably: this new meta-algorithm for sequence learning can deal both with linear algorithms and with kernel-based algorithms!

  • A new cache (SimpleDynamicKernelCache) has been added to avoid the need of specifying the number of expected items in the dataset. It is not specialized for any learning algorithm, so it is not the most efficient cache, but it is very easy to use.

Furthermore we also released a brand new web site www.kelp-ml.org, where you can find several tutorials and documentation about KeLP!

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.2.0!


Logo r-cran-CORElearn 1.50.3

by r-cran-robot - March 28, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 18529 views, 3834 downloads, 2 subscriptions

About: Classification, Regression and Feature Evaluation

Changes:

Fetched by r-cran-robot on 2017-07-01 00:00:02.795973


Showing Items 11-20 of 645 on page 2 of 65: Previous 1 2 3 4 5 6 7 Next Last