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Logo A Pattern Recognizer In Lua with ANNs v0.4.1

by pakozm - December 3, 2015, 15:01:36 CET [ Project Homepage BibTeX Download ] 12715 views, 2854 downloads, 2 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 PyML a python machine learning library focused on kernel methods 0.7.0

by asa - May 29, 2008, 22:23:39 CET [ Project Homepage BibTeX Download ] 10979 views, 2853 downloads, 0 comments, 0 subscriptions

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About: PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods.

Changes:

Initial Announcement on mloss.org.


Logo libstb 1.8

by wbuntine - April 24, 2014, 09:02:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14594 views, 2844 downloads, 1 subscription

About: Generalised Stirling Numbers for Pitman-Yor Processes: this library provides ways of computing generalised 2nd-order Stirling numbers for Pitman-Yor and Dirichlet processes. Included is a tester and parameter optimiser. This accompanies Buntine and Hutter's article: http://arxiv.org/abs/1007.0296, and a series of papers by Buntine and students at NICTA and ANU.

Changes:

Moved repository to GitHub, and added thread support to use the main table lookups in multi-threaded code.


Logo r-cran-C50 0.1.1

by r-cran-robot - November 20, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 13024 views, 2827 downloads, 0 subscriptions

About: C5.0 Decision Trees and Rule-Based Models

Changes:

Fetched by r-cran-robot on 2018-04-01 00:00:06.943492


Logo Debellor 1.0

by mwojnars - July 30, 2009, 16:48:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11956 views, 2822 downloads, 1 subscription

About: Debellor is a scalable and extensible platform which provides common architecture for data mining and machine learning algorithms of various types.

Changes:
  • Naming of numerous classes/methods/fields changed to be more accurate and comprehensible
  • Weka and Rseslib libraries updated to the newest versions: Weka 3.6.1 & Rseslib 3.0.1. Debellor's wrappers adapted
  • New class: CrossValidation - evaluator of trainable cells through cross-validation
  • New class: RMSE - calculation of Root Mean Squared Error score
  • Data objects can be compared and used in collections
  • ArffReader can read from a user-provided java.io.InputStream
  • More convenient use of parameters (setting values)
  • More convenient use of data objects and data types (construction, type casting)
  • Other minor improvements to existing classes
  • Javadoc extended

Logo 1SpectralClustering 1.1

by tbuehler - June 27, 2011, 10:45:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14333 views, 2814 downloads, 1 subscription

About: A fast and scalable graph-based clustering algorithm based on the eigenvectors of the nonlinear 1-Laplacian.

Changes:
  • fixed bug occuring when input graph is disconnected
  • reduced memory usage when input graph has large number of disconnected components
  • more user-friendly usage of main method OneSpectralClustering
  • faster computation of eigenvector initialization + now thresholded according to multicut-criterion
  • several internal optimizations

Logo JMLR RL Glue and Codecs -- Glue 3.x and Codecs

by btanner - October 12, 2009, 07:50:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25604 views, 2814 downloads, 1 subscription

About: RL-Glue allows agents, environments, and experiments written in Java, C/C++, Matlab, Python, and Lisp to inter operate, accelerating research by promoting software re-use in the community.

Changes:

RL-Glue paper has been published in JMLR.


Logo BioSig for Octave and Matlab 2.31

by schloegl - July 28, 2009, 13:41:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17012 views, 2806 downloads, 0 subscriptions

About: BioSig is a software library for biomedical signal processings. Besides several other modules, one modul (t400) provides a common interface (train_sc.m and test_sc.m) to various classification [...]

Changes:

Update of project information: machine learning and classification tools are moved to the NaN-toolbox.


Logo JMLR LIBLINEAR 1.32

by biconnect - September 3, 2008, 17:35:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 24225 views, 2803 downloads, 2 subscriptions

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(based on 2 votes)

About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM

Changes:

Initial Announcement on mloss.org.


Logo monte python 0.1.0

by roro - May 9, 2008, 21:45:47 CET [ Project Homepage BibTeX Download ] 7748 views, 2790 downloads, 1 subscription

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.

Changes:

Initial Announcement on mloss.org.


Showing Items 151-160 of 672 on page 16 of 68: First Previous 11 12 13 14 15 16 17 18 19 20 21 Next Last