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Showing Items 161-170 of 570 on page 17 of 57: First Previous 12 13 14 15 16 17 18 19 20 21 22 Next Last

About: RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in Reinforcement Learning. It is an optimized library for robotic applications and embedded devices that operates under fast duty cycles (e.g., < 30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, physical NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behaviors, and represent learnable knowledge. The implementation of the RLLib library is inspired by the RLPark API, which is a library of temporal-difference learning algorithms written in Java.

Changes:

Current release version is v2.0.


Logo SMIDAS 1.1

by ambujtewari - August 15, 2010, 18:51:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7172 views, 1436 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 r-cran-mvpart 1.6-0

by r-cran-robot - February 19, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 7151 views, 1439 downloads, 1 subscription

About: Multivariate partitioning

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.387032


Logo LibSGDQN 1.1

by antojne - July 2, 2009, 15:02:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7130 views, 1426 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 ] 7120 views, 1486 downloads, 1 subscription

About: L1 constrained estimation aka `lasso'

Changes:

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


Logo RLS2 MATLAB Toolbox 0.7

by posaune - March 31, 2010, 20:37:11 CET [ Project Homepage BibTeX Download ] 7090 views, 1501 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)

Logo scikits.learn 0.6

by fabianp - December 22, 2010, 11:58:30 CET [ Project Homepage BibTeX Download ] 7017 views, 1350 downloads, 1 subscription

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About: Obsolete. Use https://mloss.org/software/view/240/ instead.

Changes:

0.6 release


Logo SCD 2.1

by ambujtewari - December 3, 2009, 22:21:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7016 views, 1345 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.


Logo JMLR CAM Java 3.1

by wangny - October 14, 2013, 22:46:03 CET [ Project Homepage BibTeX Download ] 6988 views, 3060 downloads, 1 subscription

About: The CAM R-Java software provides a noval way to solve blind source separation problem.

Changes:

In this version, we fix the problem of not working under newest R version R-3.0.


Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6939 views, 2500 downloads, 2 subscriptions

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Changes:

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

  1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
  2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
  3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
  4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.


Showing Items 161-170 of 570 on page 17 of 57: First Previous 12 13 14 15 16 17 18 19 20 21 22 Next Last