About: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model

Fetched by r-cran-robot on 2013-04-01 00:00:05.305206

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression.

Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.

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: MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text. It was written primarily by William W. Cohen, a professor at [...]

Initial Announcement on mloss.org.

About: Visualizing the performance of scoring classifiers.

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

About: A Matlab implementation of Sparse PCA using the inverse power method for nonlinear eigenproblems.

• Added deflation scheme to compute multiple principal components
• Several internal runtime and memory optimizations
• API change: sparsePCA.m is now used to compute multiple components; use computeTradeOffCurve.m to reproduce the examples in the NIPS paper

About: Easily prototype WEKA classifiers and filters using Python scripts.

0.3.0

• Filters have now been implemented.
• Classifier and filter classes satisfy base unit tests.

0.2.1

• Can now choose to save the script in the model using the -save flag.

0.2.0

• Added Python 3 support.
• Added uses decorator to prevent non-essential arguments from being passed.
• Fixed nasty bug where imputation, binarisation, and standardisation would not actually be applied to test instances.
• GUI in WEKA now displays the exception as well.
• Fixed bug where single quotes in attribute values could mess up args creation.
• ArffToPickle now recognises class index option and arguments.
• Fix nasty bug where filters were not being saved and were made from scratch from test data.

0.1.1

• ArffToArgs gets temporary folder in a platform-independent way, instead of assuming /tmp/.
• Can now save args in ArffToPickle using save.

0.1.0

• Initial release.

About: Fitting user specified models with Group Lasso penalty

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

About: TinyOS is a small operating for small (wireless) sensors. LEGO MINDSTORMS NXT is a platform for embedded systems experimentation: The combination of NXT and TinyOS is NXTMOTE.

Initial Announcement on mloss.org.

About: LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs.

small bug fix (thx nicolas ;)

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.

• a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
• support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
• support for label powerset based stratified kfold added
• graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
• bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

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: OpenCog aims to provide research scientists and software developers with a common platform to build and share artificial intelligence programs. The long-term goal of OpenCog is acceleration of the [...]

Initial Announcement on mloss.org.

About: Monte (python) is a small machine learning library written in pure Python. The focus is on gradient based learning, in particular on the construction of complex models from many smaller components.

Initial Announcement on mloss.org.

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: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Moved project to GitHub.

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: Shrinkage Discriminant Analysis and CAT Score Variable Selection

Fetched by r-cran-robot on 2012-02-01 00:00:11.559491