About: LogReg-Crowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979).

Initial Announcement on mloss.org. Added GitHub page.

About: Lush is an object-oriented Lisp dialect with a super-simple way of integrating C/C++ code and libraries. It includes extensive libraries for numerical computing, machine learning, and computer [...]

Initial Announcement on mloss.org.

About: A (randomized) coordinate descent procedure to minimize L1 regularized loss for classification and regression purposes.

Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.

About: GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for parameter estimation and inference. GibbsLDA++ is fast and is designed to analyze hidden/latent topic [...]

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: A chatterbot that learns natural languages learning from imitation.

Alpha 1 - Codename: Wendell Borton ("Bllluuhhhhh...!!")

Short term memory greatly improved.

About: The SGD-2.0 package contains implementations of the SGD and ASGD algorithms for linear SVMs and linear CRFs.

Version 2.0 features ASGD.

About: R genetic programming framework

Fetched by r-cran-robot on 2013-04-01 00:00:08.163887

About: Toolbox for circular statistics with Matlab (The Mathworks).

Some bugfixes.

About: TBEEF, a doubly ensemble framework for recommendation and prediction problems.

Included the final technical report.

About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

• Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

• Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

• Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.

About: Bayesian Additive Regression Trees

Fetched by r-cran-robot on 2018-09-01 00:00:04.021726

About: The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets.

Initial Announcement on mloss.org.

About: Reference implementation of the LASVM online and active SVM algorithms as described in the JMLR paper. The interesting bit is a small C library that implements the LASVM process and reprocess [...]

Minor bug fix

About: The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way.

Fixes for shogun 0.7.3.

About: Accurate splice site predictor for a variety of genomes.

Asp now supports three formats:

-g fname for gff format

-s fname for spf format

-b dir for a binary format compatible with mGene.

And a new switch

-t which switches on a sigmoid-based transformation of the svm scores to get scores between 0 and 1.

About: Least Angle Regression, Lasso and Forward Stagewise

Fetched by r-cran-robot on 2013-04-01 00:00:05.906244

About: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from large-scale data.

Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.

About: PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods.

Initial Announcement on mloss.org.