20 projects found that use the gnu gpl v3 license.
Showing Items 1-20 of 42 on page 1 of 3: 1 2 3 Next

Logo contextual 0.9.8

by robinvanemden - February 10, 2019, 16:32:57 CET [ Project Homepage BibTeX Download ] 788 views, 268 downloads, 2 subscriptions

About: R package facilitating the simulation and evaluation of context-free and contextual Multi-Armed Bandit policies.

Changes:

Major update: Offline Bandit API overhaul - now makes use of R formulae. More demo R scripts added. New Contextual Bandits and Policies. Bug fixes.


Logo Somoclu 1.7.5

by peterwittek - March 1, 2018, 23:30:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 46097 views, 8408 downloads, 0 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

Changes:
  • New: A Makefile for mingw to build on Windows.
  • Changed: PR #94 added a much more efficient sparse kernel.
  • Changed: boilerplate code for Julia greatly improved.
  • Changed: Code cleanup, pre-processor macros simplified.
  • Changed: Adapted to Seaborn API changes in plotting heatmaps.

Logo python weka wrapper3 0.1.4

by fracpete - February 18, 2018, 04:54:03 CET [ Project Homepage BibTeX Download ] 8056 views, 1916 downloads, 0 subscriptions

About: A thin Python3 wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • upgraded to Weka 3.9.2
  • properly initializing package support now, rather than adding package jars to classpath
  • added weka.core.ClassHelper Java class for obtaining classes and static fields, as javabridge only uses the system class loader

Logo python weka wrapper 0.3.12

by fracpete - February 18, 2018, 04:29:24 CET [ Project Homepage BibTeX Download ] 71024 views, 15118 downloads, 0 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • upgraded to Weka 3.9.2
  • properly initializing package support now, rather than adding package jars to classpath
  • added weka.core.ClassHelper Java class for obtaining classes and static fields, as javabridge only uses the system class loader

Logo ADAMS 17.12.0

by fracpete - December 20, 2017, 09:38:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40741 views, 7268 downloads, 0 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.

Changes:

Some highlights:

  • Code base was moved to Github
  • Nearly 90 new actors, 25 new conversions
  • much improved deeplearning4j module
  • experimental support for Microsoft's CNTK deep learning framework
  • rsync module
  • MEKA webservice module
  • improved support for image annotations
  • improved LaTeX support
  • Websocket support

Logo iLANN SVD. An incremental noniterative learning method for one layer feedforwar 1.0

by ofontenla - August 16, 2017, 11:53:40 CET [ BibTeX BibTeX for corresponding Paper Download ] 1871 views, 525 downloads, 0 subscriptions

About: A non-iterative, incremental and hyperparameter-free learning method for one-layer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition.

Changes:

Initial Announcement on mloss.org.


Logo LANN SVD. A noniterative SVD based learning algorithm for one layer neural nets 1.0

by ofontenla - August 7, 2017, 13:52:19 CET [ BibTeX BibTeX for corresponding Paper Download ] 1794 views, 514 downloads, 0 subscriptions

About: A non-iterative learning method for one-layer (no hidden layer) neural networks, where the weights can be calculated in a closed-form manner, thereby avoiding low convergence rate and also hyperparameter tuning. The proposed learning method, LANN-SVD in short, presents a good computational efficiency for large-scale data analytic.

Changes:

Initial Announcement on mloss.org.


Logo A framework for benchmarking of feature selection algorithms and cost functions v1.3

by msreis - August 4, 2017, 00:19:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3451 views, 621 downloads, 0 subscriptions

About: An open-source framework for benchmarking of feature selection algorithms and cost functions.

Changes:

Initial Announcement on mloss.org.


Logo OpenNN 3.1

by Sergiointelnics - March 3, 2017, 17:17:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15857 views, 2483 downloads, 0 subscriptions

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.

Changes:

New algorithms, correction of bugs.


Logo opusminer 0.1-0

by opusminer - February 23, 2017, 01:01:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2617 views, 471 downloads, 0 subscriptions

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.

Changes:

Initial Announcement on mloss.org.


Logo LogRegCrowds, Logistic Regression from Crowds 1.0

by fmpr - January 16, 2017, 18:10:57 CET [ Project Homepage BibTeX Download ] 6165 views, 1568 downloads, 0 subscriptions

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).

Changes:

Initial Announcement on mloss.org. Added GitHub page.


Logo Java Statistical Analysis Tool 0.0.7

by EdwardRaff - January 15, 2017, 22:21:50 CET [ Project Homepage BibTeX Download ] 5744 views, 1372 downloads, 0 subscriptions

About: General purpose Java Machine Learning library for classification, regression, and clustering.

Changes:

See github release tab for change info


Logo slim for matlab 0.2

by ustunb - August 23, 2016, 20:27:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4293 views, 919 downloads, 0 subscriptions

About: learn optimized scoring systems using MATLAB and the CPLEX Optimization Studio

Changes:

Initial Announcement on mloss.org.


Logo Sparse Compositional Metric Learning v1.11

by bellet - August 2, 2016, 11:43:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 9158 views, 2720 downloads, 0 subscriptions

About: Scalable learning of global, multi-task and local metrics from data

Changes:

Minor bug fix in multi-task objective computation (thanks to Junjie Hu).


Logo Multiagent Decision Process Toolbox 0.4

by faoliehoek - June 2, 2016, 17:38:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6840 views, 1546 downloads, 0 subscriptions

About: The Multiagent decision process (MADP) Toolbox is a free C++ software toolbox for scientific research in decision-theoretic planning and learning in multiagent systems.

Changes:

-Includes freshly written spirit parser for .pomdp files. -Includes new code for pruning POMDP vectors; obviates dependence on Cassandra's code and old LP solve version. -Includes new factor graph solution code -Generalized firefighting CGBG domain added -Simulation class for Factored Dec-POMDPs and TOI Dec-MDPs -Approximate BG clustering methods and kGMAA with clustering.


Logo AutoWEKA 2.0

by larsko - May 19, 2016, 19:58:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4325 views, 1316 downloads, 0 subscriptions

About: Automatically finds the best model with its best parameter settings for a given classification or regression task.

Changes:

Initial Announcement on mloss.org.


Logo JaTeCS 1.0.0

by aesuli - April 5, 2016, 17:23:12 CET [ Project Homepage BibTeX Download ] 4088 views, 969 downloads, 0 subscriptions

About: Jatecs is an open source Java library focused on automatic text categorization.

Changes:

Initial Announcement on mloss.org.


Logo A Pattern Recognizer In Lua with ANNs v0.4.1

by pakozm - December 3, 2015, 15:01:36 CET [ Project Homepage BibTeX Download ] 13936 views, 3187 downloads, 0 subscriptions

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.

Changes:
  • 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.

Logo A Library for Online Streaming Feature Selection 1.0

by ykui713 - November 25, 2015, 13:23:01 CET [ BibTeX Download ] 2752 views, 1138 downloads, 0 subscriptions

About: LOFS is a software toolbox for online streaming feature selection

Changes:

Initial Announcement on mloss.org.


Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 3379 views, 777 downloads, 0 subscriptions

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Changes:

Initial Announcement on mloss.org.


Showing Items 1-20 of 42 on page 1 of 3: 1 2 3 Next