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Logo asp 0.3

by sonne - May 7, 2010, 10:25:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11648 views, 2265 downloads, 1 subscription

About: Accurate splice site predictor for a variety of genomes.

Changes:

Asp now supports three formats:

-g fname for gff format

-s fname for spf format

-b dir for a binary format compatible with mGene.

And a new switch

-t which switches on a sigmoid-based transformation of the svm scores to get scores between 0 and 1.


Logo Piqle 2.0

by fdecomite - June 19, 2009, 10:16:53 CET [ Project Homepage BibTeX Download ] 5239 views, 2254 downloads, 1 subscription

About: Piqle (Platform for Implementing Q-Learning Experiments) is a Java framework for fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms and problems, it allows users to focus on either part of the RL paradigm:designing new algorithms or implementing new problems. Piqle implements many classical RL algorithms, making their parameters easily tunable. At this time, 13 problems are implemented, several with one or more variants. The user's manual explains in detail how to code a new problem. Written in Java, Piqle is as platform-independent as Java itself. Its components can easily be embedded as part of complex implementations, like robotics or decision making.

Changes:

Initial Announcement on mloss.org.


Logo pyGPs 1.3.2

by mn - January 17, 2015, 13:08:43 CET [ Project Homepage BibTeX Download ] 9885 views, 2250 downloads, 4 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

Changelog pyGPs v1.3.2

December 15th 2014

  • pyGPs added to pip
  • mathematical definitions of kernel functions available in documentation
  • more error message added

Logo r-cran-hda 0.2-11

by r-cran-robot - February 17, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 10999 views, 2250 downloads, 0 subscriptions

About: Heteroscedastic Discriminant Analysis

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.551691


Logo A Pattern Recognizer In Lua with ANNs v0.4.1

by pakozm - December 3, 2015, 15:01:36 CET [ Project Homepage BibTeX Download ] 9924 views, 2246 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 MOSIS 0.55

by claasahl - March 9, 2014, 17:35:40 CET [ BibTeX Download ] 7437 views, 2241 downloads, 2 subscriptions

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications.

Changes:
  • Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
  • Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
  • Refactored tests for data formats (e.g. PlainText and JSON tests).
  • Added visitor design pattern for graph-based functions (e.g. initialization and processing).
  • Documented parameters of Processor implementations.

Logo Spider 1.71

by jaseweston - November 19, 2007, 15:51:59 CET [ Project Homepage BibTeX Download ] 7423 views, 2239 downloads, 0 subscriptions

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About: The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be [...]

Changes:

Initial Announcement on mloss.org.


Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13841 views, 2238 downloads, 2 subscriptions

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

Changes:

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999


Logo Oger 1.1.3

by dvrstrae - August 13, 2012, 14:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5537 views, 2238 downloads, 1 subscription

About: The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets.

Changes:

Initial Announcement on mloss.org.


Logo LSTM for biological sequence analysis 1.0

by mhex - July 28, 2010, 16:32:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9474 views, 2232 downloads, 1 subscription

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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.

Changes:

Spectrum LSTM package included


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