About: A Tool for Measuring String Similarity

This release fixes the incorrect implementation of the bag distance.

About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Corrected the new normalised Gamma model for topics so it works with multicore. Improvements to documentation. Added an asymptotic version of the generalised Stirling numbers so it longer fails when they run out of bounds on bigger data.

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: Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition

Initial Announcement on mloss.org.

About: This is a tool for retrieving nearest neighbors and clustering of large categorical data sets represented in transactional form. The clustering is achieved via a locality-sensitive hashing of categorical datasets for speed and scalability.

Initial Announcement on mloss.org.

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

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

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.

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

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

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

Initial Announcement on mloss.org.

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.

About: Hubness-aware Machine Learning for High-dimensional Data

• BibTex support for all algorithm implementations, making all of them easy to reference (via algref package).

• Two more hubness-aware approaches (meta-metric-learning and feature construction)

• An implementation of Hit-Miss networks for analysis.

• Several minor bug fixes.

• The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF.

• The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k.

• Some more experimental scripts have been included.

• Extensions in the estimation of hubness risk.

• Alias and weighted reservoir methods for weight-proportional random selection.

About: Hype is a proof-of-concept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization.

Initial Announcement on mloss.org.

About: Hyperstream is a large-scale, flexible and robust software package for processing streaming data.

python 3 support; new API; bug fixes and enhancements