About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.

Initial Announcement on mloss.org.

About: SVM Toolbox fully written in Matlab (even the QP solver). Features : SVM, MultiClassSVM, One-Class, SV Regression, AUC-SVM and Rankboost, 1-norm SVM, Regularization Networks, Kernel Basis Pursuit [...]

Initial Announcement on mloss.org.

About: This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...]

Initial Announcement on mloss.org.

About: A comprehensive data mining environment, with a variety of machine learning components.

Modifications following feedback from Knime main Author.

About: Fitting user specified models with Group Lasso penalty

Fetched by r-cran-robot on 2013-04-01 00:00:05.428021

About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Release 0.7.0

• Update to TensorFlow r1.4.

• Tutorials in the documentation on:

• Interfacing with Keras

• Saving/loading models

• How to build a variety of regressors with Aboleth

• New prediction module with some convenience functions, including freezing the weight samples during prediction.

• Bayesian convolutional layers with accompanying demo.

• Allow the number of samples drawn from a model to be varied by using placeholders.

• Generalise the feature embedding layers to work on matrix inputs (instead of just column vectors).

• Numerous numerical and usability fixes.

About: Java package implementing a kernel for (molecular) graphs based on iterative graph similarity and optimal assignments.

Initial Announcement on mloss.org.

About: An audio editing tool for single-channel source separation.

Stereo processing, bug fixes, UI updates.

About: Encog is a Machine Learning framework for Java, C#, Javascript and C/C++ that supports SVM's, Genetic Programming, Bayesian Networks, Hidden Markov Models and other algorithms.

Changes for Encog 3.2:

Issue #53: Fix Out Of Range Bug In BasicMLSequenceSet. Issue #52: Unhandled exception in Encog.Util.File.ResourceLoader.CreateStream (ResourceLoader.cs) Issue #50: Concurrency bugs in PruneIncremental Issue #48: Unit Tests Failing - TestHessian Issue #46: Couple of small fixes - Temporal DataSet and SCG training Issue #45: Fixed EndMinutesStrategy to correctly evaluate ShouldStop after the specified number of minutes have elapsed. Issue #44: Encog.ML.Data.Basic.BasicMLDataPairCentroid.Add() & .Remove() Issue #43: Unit Tests Failing - Matrix not full rank Issue #42: Nuget - NuSpec Issue #36: Load Examples easier

About: Bayesian Additive Regression Trees

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

About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM.

Initial Announcement on mloss.org.

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

This version introduces a number of new features:

• The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

• The parser now outputs dependency labels along with the backbone structure.

• As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

• The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.

About: Message passing for topic modeling

1. improve "readme.pdf".
2. correct some compilation errors.

About: Matlab code for performing variational inference in the Indian Buffet Process with a linear-Gaussian likelihood model.

Initial Announcement on mloss.org.

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

Updated mloss entry (minor fixes).

About: Visualizing the performance of scoring classifiers.

Fetched by r-cran-robot on 2013-04-01 00:00:08.344833

About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included.

Spectrum LSTM package included

About: A Toolkit for Recursive Partytioning

Fetched by r-cran-robot on 2013-04-01 00:00:06.838561

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: A chatterbot that learns natural languages learning from imitation.

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

Short term memory greatly improved.