About: APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.

• Updated home repository link to follow april-org github organization.
• Improved serialize/deserialize functions, reimplemented all the serialization procedure.
• Added exceptions support to LuaPkg and APRIL-ANN, allowing to capture C++ errors into Lua code.
• Added set class.
• Added series class.
• Added data_frame class, similar to Python Pandas DataFrame.
• Serialization and deserilization have been updated with more robust and reusable API, implemented in util.serialize() and util.deserialize() functions.
• Added matrix.ext.broadcast utility (similar to broadcast in numpy).
• Added ProbablisitcMatrixANNComponent, which allow to implement probabilistic mixtures of posteriors and/or likelihoods.
• Added batch normalization ANN component.
• Allowing matrix.join to add new axis.
• Added methods prod(), cumsum() and cumprod() at matrix classes.
• Added methods count_eq() and count_neq() at matrix classes.
• Serializable objects API have been augmented with methods ctor_name() and
  ctor_params() in Lua, refered to luaCtorName() and luaCtorParams() in C++.
• Added cast.to to dynamic cast C++ objects pushed into Lua, allowing to convert base class objects into any of its derived classes.
• Added matrix.sparse as valid values for targets in ann.loss.mse and
  ann.loss.cross_entropy.
• Changed matrix metamethods __index and __newindex, allowing to use
  matrix objects with standard Lua operator[].
• Added matrix.masked_fill and matrix.masked_copy matrix.
• Added matrix.indexed_fill and matrix.indexed_copy matrix.
• Added ann.components.probabilistic_matrix, and its corresponding specializations ann.components.left_probabilistic_matrix and
  ann.components.right_probabilistic_matrix.
• Added operator[] in the right side of matrix operations.
• Added ann.components.transpose.
• Added max_gradients_norm in traianble.supervised_trainer, to avoid gradients exploding.
• Added ann.components.actf.sparse_logistic a logistic activation function with sparsity penalty.
• Simplified math.add, math.sub, ... and other math extensions for reductions, their original behavior can be emulated by using bind function.
• Added bind function to freeze any positional argument of any Lua function.
• Function stats.boot uses multiple_unpack to allow a table of sizes and the generation of multiple index matrices.
• Added multiple_unpack Lua function.
• Added __tostring metamethod to numeric memory blocks in Lua.
• Added dataset.token.sparse_matrix, a dataset which allow to traverse by rows a sparse matrix instance.
• Added matrix.sparse.builders.dok, a builder which uses the Dictionary-of-Keys format to construct a sparse matrix from scratch.
• Added method data to numeric matrix classes.
• Added methods values, indices, first_index to sparse matrix class.
• Fixed bugs when reading bad formed CSV files.
• Fixed bugs at statistical distributions.
• FloatRGB bug solved on equal (+=, -=, ...) operators. This bug affected ImageRGB operations such as resize.
• Solved problems when chaining methods in Lua, some objects end to be garbage collected.
• Improved support of strings in auto-completion (rlcompleter package).
• Solved bug at SparseMatrix<T> when reading it from a file.
• Solved bug in Image<T>::rotate90_cw methods.
• Solved bug in SparseMatrix::toDense() method.

C/C++

• Better LuaTable accessors, using [] operator.
• Implementation of matrix __index, __newindex and __call metamethods in C++.
• Implementation of matProd(), matCumSum() and matCumProd() functions.
• Implementation of matCountEq() and matCountNeq() functions for
  Matrix<T>.
• Updated matrix_ext_operations.h to change API of matrix operations. All functions have been overloaded to accept an in-place operation and another version which receives a destination matrix.
• Adding iterators to language models.
• Added MatrixScalarMap2 which receives as input2 a SparaseMatrix instance. This functions needs to be generalized to work with CPU and CUDA.
• The method SparseMatrix<T>::fromDenseMatrix() uses a DOKBuilder object to build the sparse matrix.
• The conversion of a Matrix<T> into a SparseMatrix<T> has been changed from a constructor overload to the static method
  SparseMatrix<T>::fromDenseMatrix().
• Added support for IPyLua.
• Optimized matrix access for confusion matrix.
• Minor changes in class.lua.
• Improved binding to avoid multiple object copies when pushing C++ objects.
• Added Git commit hash and compilation time.

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier.

• minor bugfix

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.

This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

About: This package contains a python and a matlab implementation of the most widely used algorithms for multi-armed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies.

Initial Announcement on mloss.org.

About: Multivariate partitioning

Fetched by r-cran-robot on 2013-04-01 00:00:06.387032

About: Big Random Forests

Fetched by r-cran-robot on 2015-11-01 00:00:04.072762

About: Nimfa is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported.

Initial Announcement on mloss.org.

About: The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multi-threading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more.

Updated to version 0.3.0

About: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class [...]

Initial Announcement on mloss.org.

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

• added ElasticNets as a regularization option
• fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
• added a couple of I/O optimizations

About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA.

bug removed

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: Bayesian Additive Regression Trees

Fetched by r-cran-robot on 2018-09-01 00:00:03.597464

About: MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to [...]

MALLET 2.0 RC4 Release Notes July 16, 2009

Major updates:

An implementation of generalized expectation criteria training of MaxEnt classifiers and methods for obtaining constraints (c.f. Gregory Druck, Gideon Mann, Andrew McCallum "Learning from Labeled Features using Generalized Expectation Criteria.")

PagedInstanceList has been substantially rewritten by Mike Bond.

Bug fixes to topic model hyperparameter optimization and topic inference.

About: A generalized version of spectral clustering using the graph p-Laplacian.

various internal optimizations

About: A Matlab benchmarking toolbox for online and adaptive regression with kernels.

• Changes in algorithms' Matlab class format
• New algorithms
• Minor improvements and bug fixes