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Logo crfpp 0.53

by sonne - May 8, 2009, 08:46:44 CET [ Project Homepage BibTeX Download ] 3677 views, 2624 downloads, 1 subscription

About: CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data.

Changes:

Initial Announcement on mloss.org.


Logo Thresholding program 1.0

by openpr_nlpr - March 1, 2012, 03:18:52 CET [ Project Homepage BibTeX Download ] 3642 views, 441 downloads, 1 subscription

About: This is demo program on global thresholding for image of bright small objects, such as aircrafts in airports. the program include four method, otsu,2D-Tsallis,PSSIM, Smoothnees Method.

Changes:

Initial Announcement on mloss.org.


About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data.

Changes:

o citation update

o plot function improved


Logo Ohmm 0.02

by hillbig - May 21, 2009, 10:07:53 CET [ Project Homepage BibTeX Download ] 3516 views, 966 downloads, 1 subscription

About: Ohmm is a library for learning hidden Markov models by using Online EM algorithm. This library is specialized for large scale data; e.g. 1 million words. The output includes parameters, and estimation results.

Changes:

Initial Announcement on mloss.org.


Logo Gesture Recogition Toolkit 0.1 Revision 289

by ngillian - December 13, 2013, 22:59:53 CET [ Project Homepage BibTeX Download ] 3500 views, 651 downloads, 1 subscription

About: The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library that has been specifically designed for real-time gesture recognition. It features a large number of machine-learning algorithms for both classification and regression in addition to a wide range of supporting algorithms for pre-processing, feature extraction and dataset management. The GRT has been designed for real-time gesture recognition, but it can also be applied to more general machine-learning tasks.

Changes:

Added Decision Tree and Random Forests.


Logo Rudder 0.1

by dmcnelis - December 16, 2011, 22:00:45 CET [ Project Homepage BibTeX Download ] 3467 views, 1117 downloads, 1 subscription

About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions.

Changes:

Now supports OLS and GLS regression and NaiveBayes classification


Logo Hivemall 0.1

by myui - October 25, 2013, 08:43:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3457 views, 537 downloads, 1 subscription

About: Hivemall is a scalable machine learning library running on Hive/Hadoop, licensed under the LGPL 2.1.

Changes:
  • Enhancement

    • Added AROW regression
    • Added AROW with a hinge loss (arowh_regress())
  • Bugfix

    • Fixed a bug of null feature handling in classification/regression

Logo Pynopticon 0.1

by Wiecki - February 1, 2009, 18:55:10 CET [ Project Homepage BibTeX Download ] 3444 views, 968 downloads, 1 subscription

About: Pynopticon is a toolbox that allows you to create and train your own object recognition classifiers. It makes rapid prototyping of object recognition work flows a snap. Simply create a dataset of [...]

Changes:

Initial Announcement on mloss.org.


Logo Piqle 2.0

by fdecomite - June 19, 2009, 10:16:53 CET [ Project Homepage BibTeX Download ] 3393 views, 1723 downloads, 1 subscription

About: Piqle (Platform for Implementing Q-Learning Experiments) is a Java framework for fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms and problems, it allows users to focus on either part of the RL paradigm:designing new algorithms or implementing new problems. Piqle implements many classical RL algorithms, making their parameters easily tunable. At this time, 13 problems are implemented, several with one or more variants. The user's manual explains in detail how to code a new problem. Written in Java, Piqle is as platform-independent as Java itself. Its components can easily be embedded as part of complex implementations, like robotics or decision making.

Changes:

Initial Announcement on mloss.org.


Logo HDDM 0.5

by Wiecki - April 24, 2013, 02:53:07 CET [ Project Homepage BibTeX Download ] 3389 views, 870 downloads, 1 subscription

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.

Changes:
  • New and improved HDDM model with the following changes:
    • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
    • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
    • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
    • The old model has been renamed to HDDMTransformed.
    • HDDMRegression and HDDMStimCoding are also using this model.
  • HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
  • Improved online documentation at http://ski.clps.brown.edu/hddm_docs
  • A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
  • Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
  • Maximum likelihood optimization.
  • Many bugfixes and better test coverage.
  • hddm_fit.py command line utility is depracated.

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