All entries.
Showing Items 321-330 of 537 on page 33 of 54: First Previous 28 29 30 31 32 33 34 35 36 37 38 Next Last

Logo yaplf 0.7

by malchiod - April 22, 2010, 11:34:07 CET [ Project Homepage BibTeX Download ] 3222 views, 788 downloads, 1 subscription

About: yaplf (Yet Another Python Learning Framework) is an extensible machine learning framework written in python

Changes:

Initial Announcement on mloss.org.


Logo r-cran-sda 1.2.1

by r-cran-robot - January 22, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 3221 views, 692 downloads, 0 subscriptions

About: Shrinkage Discriminant Analysis and CAT Score Variable Selection

Changes:

Fetched by r-cran-robot on 2012-02-01 00:00:11.559491


Logo Kernel Machine Library 0.2

by pawelm - December 27, 2011, 17:14:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper ] 3220 views, 121 downloads, 1 subscription

About: The Kernel-Machine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines.

Changes:

Updated mloss entry (minor fixes).


Logo Market Basket Synthetic Data Generator v1.0.0.0

by apitman - February 9, 2011, 11:26:55 CET [ Project Homepage BibTeX Download ] 3220 views, 763 downloads, 1 subscription

About: An open-source C# market-basket synthetic data generator, capable of creating transactions, sequences and taxonomies, based on the IBM Quest version. Written to address the maintainability and portability problems of the original, feedback, fixes and extensions are encouraged!

Changes:

Initial Announcement on mloss.org.


Logo python weka wrapper 0.1.10

by fracpete - August 29, 2014, 05:00:14 CET [ Project Homepage BibTeX Download ] 3206 views, 642 downloads, 2 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • fixed adding custom classpath using jvm.start(class_path=[...])

Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3200 views, 540 downloads, 2 subscriptions

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

Changes:

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999


Logo NetPro 1.1.17

by lml - January 25, 2011, 19:02:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3193 views, 759 downloads, 1 subscription

About: Tools for functional network analysis.

Changes:

Initial Announcement on mloss.org.


Logo LHOTSE 0.14

by mseeger - November 26, 2007, 21:12:19 CET [ Project Homepage BibTeX ] 3192 views, 26 downloads, 0 comments, 0 subscriptions

About: *LHOTSE* is a C++ class library designed for the implementation of large, efficient scientific applications in Machine Learning and Statistics.

Changes:

Initial Announcement on mloss.org.


About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models.

Changes:

Code restructure and bug fix.


Logo HDDM 0.5

by Wiecki - April 24, 2013, 02:53:07 CET [ Project Homepage BibTeX Download ] 3159 views, 828 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.

Showing Items 321-330 of 537 on page 33 of 54: First Previous 28 29 30 31 32 33 34 35 36 37 38 Next Last