About: This library provides Python functions for agglomerative clustering. Its features include

Initial Announcement on mloss.org.

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.

• 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.

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

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 [...]

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 [...]

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.

Initial Announcement on mloss.org.

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

• Enhancement

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

• Bugfix

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

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

Updated to version 1.0

About: Matlab code for semi-supervised regression and dimensionality reduction using Hessian energy.

Initial Announcement on mloss.org.

About: HSSVM is a software for solving multi-class problem using Hyper-sphere Support Vector Machines model, implemented by Java.

1. From this version, the version number is normalized to hssvm1.0.1;
2. In this version, we delete the features about running parameter searching and run-all from Ant script, that is, commands "ant search-param" and "ant run-all" which exist in previous version are no longer available, and they are replaced with commands "svm search conf" and "svm runall conf", both of them are used on Linux(or all other POSIX systems).If you want to use this program on Windows, the cygwin is required to be installed.