About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.

In addition to minor bug fixes, this release includes:

• A new learning algorithm that enable (for the first time in KeLP) to deal with sequences labeling problems! It is based on a Markovian formulation within a SVM framework. Most noticeably: this new meta-algorithm for sequence learning can deal both with linear algorithms and with kernel-based algorithms!

• A new cache (SimpleDynamicKernelCache) has been added to avoid the need of specifying the number of expected items in the dataset. It is not specialized for any learning algorithm, so it is not the most efficient cache, but it is very easy to use.

Furthermore we also released a brand new web site www.kelp-ml.org, where you can find several tutorials and documentation about KeLP!

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.2.0!

About: Classification, Regression and Feature Evaluation

Fetched by r-cran-robot on 2017-07-01 00:00:02.795973

About: Mining Association Rules and Frequent Itemsets

Fetched by r-cran-robot on 2017-07-01 00:00:01.820822

About: Armadillo is a high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. The function syntax is deliberately similar to MATLAB. Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products).

• more accurate sparse eigen decomposition by eigs_sym() and eigs_gen()
• more robust handling of non-square matrices by lu()
• expanded qz() to optionally specify ordering of the Schur form
• expanded .each_slice() in the Cube class to support matrix multiplication
• expanded several functions to handle sparse matrices
• added expmat_sym(), logmat_sympd(), sqrtmat_sympd() for handling symmetric matrices
• added polyfit() and polyval() for polynomial fitting
• fix for aliasing issue in convolution functions conv() and conv2()
• fix for memory leak in the field class when compiling in C++11/C++14 mode

About: OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. The library has been designed to learn from both data sets and mathematical models.

New algorithms, correction of bugs.

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, Renyi entropy/mutual information, and weighted variants of Shannon entropies/mutual informations. Works with discrete distributions, and expects column vectors of features.

Fixed a Windows compilation bug. MIToolbox v3 should now compile using Visual Studio.

About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly

Fetched by r-cran-robot on 2017-07-01 00:00:03.324561

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: The new R package opusminer provides an R interface to the OPUS Miner algorithm (implemented in C++) for finding the key associations in transaction data efficiently, in the form of self-sufficient itemsets, using either leverage or lift.

Initial Announcement on mloss.org.