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Logo HDDM 0.5

by Wiecki - April 24, 2013, 02:53:07 CET [ Project Homepage BibTeX Download ] 5534 views, 1381 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.

Logo Hidden Markov Support Vector Machines 0.2

by pramod - April 16, 2010, 17:27:41 CET [ BibTeX Download ] 6359 views, 1664 downloads, 1 subscription

About: This software is an implementation of Hidden Markov Support Vector Machines (HMSVMs).

Changes:

Initial Announcement on mloss.org.


Logo HierLearning 1.0

by neville - March 2, 2014, 04:24:37 CET [ BibTeX BibTeX for corresponding Paper Download ] 2210 views, 618 downloads, 1 subscription

About: HierLearning is a C++11 implementation of a general-purpose, multi-agent, hierarchical reinforcement learning system for sequential decision problems.

Changes:

Initial Announcement on mloss.org.


About: The High-Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifier for high-dimensional data. This classifier is based on a mixture of Gaussian models adapted for [...]

Changes:

Initial Announcement on mloss.org.


About: The High Dimensional Discriminant Analysis (HDDA) toolbox contains an efficient supervised classifier for high-dimensional data. This classifier is based on Gaussian models adapted for [...]

Changes:

Initial Announcement on mloss.org.


Logo Histogram of Oriented Gradient 1.0

by openpr_nlpr - February 10, 2015, 08:27:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1712 views, 334 downloads, 2 subscriptions

About: This is an exact implementation of Histogram of Oriented Gradient as mentioned in the paper by Dalal.

Changes:

Initial Announcement on mloss.org.


About: This program is used to extract HOG(histograms of oriented gradients) features from images. The integral histogram is used for fast histogram extraction. Both APIs and binary utility are provided.

Changes:

Initial Announcement on mloss.org.


Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8905 views, 1533 downloads, 3 subscriptions

About: Hivemall is a scalable machine learning library running on Hive/Hadoop.

Changes:
  • Supported Matrix Factorization
  • Added a support for TF-IDF computation
  • Supported AdaGrad/AdaDelta
  • Supported AdaGradRDA classification
  • Added normalization scheme

Logo HLearn 1.0

by mikeizbicki - May 9, 2013, 05:58:18 CET [ Project Homepage BibTeX Download ] 5112 views, 1328 downloads, 1 subscription

About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure

Changes:

Updated to version 1.0


About: Jie Gui et al., "How to estimate the regularization parameter for spectral regression discriminant analysis and its kernel version?", IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 2, pp. 211-223, 2014

Changes:

Initial Announcement on mloss.org.


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