Projects that are tagged with classification.
Showing Items 1-20 of 75 on page 1 of 4: 1 2 3 4 Next

Logo KeLP 1.2.1

by kelpadmin - July 24, 2015, 15:43:13 CET [ Project Homepage BibTeX Download ] 2199 views, 532 downloads, 3 subscriptions

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 classifiers without writing a single line of code.

Changes:

The code for learning relations between pairs of short texts has been released, and includes the approach described in:

Simone Filice, Giovanni Da San Martino and Alessandro Moschitti. Relational Information for Learning from Structured Text Pairs. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015.

In particular this new release includes:

  • TreePairRelTagger: a manipulator that establishes relations between two tree representations (available in the maven project discreterepresentation)

  • 5 new kernels on pairs: released in the maven project standard-kernel

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 1.2.1!


About: Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU, etc. The toolbox is a user-friendly open source software and is freely available on the website.

Changes:

New features

  • GPU support (about 5 times faster than CPU - test in GPU: NVIDEA GeForce GTX 780 CPU: AMD FX 8150 Eight-Core 3.6 GHz)
  • Cast DBN parameters to single and double data types
  • Sparsity in RBM with three different methods
  • Plotting bases function
  • Classification and feature extraction on 20 Newsgroups datasets
  • Code correction in using back propagation.
  • Runtime and memory code optimization in Normalization and Shuffling

cardinal


Logo JMLR GPstuff 4.6

by avehtari - July 15, 2015, 15:08:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22082 views, 5288 downloads, 2 subscriptions

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2015-07-09 Version 4.6

Development and release branches available at https://github.com/gpstuff-dev/gpstuff

New features

  • Use Pareto smoothed importance sampling (Vehtari & Gelman, 2015) for

  • importance sampling leave-one-out cross-validation (gpmc_loopred.m)

  • importance sampling integration over hyperparameters (gp_ia.m)

  • importance sampling part of the logistic Gaussian process density estimation (lgpdens.m)

  • references:

    • Aki Vehtari and Andrew Gelman (2015). Pareto smoothed importance sampling. arXiv preprint arXiv:1507.02646.
    • Aki Vehtari, Andrew Gelman and Jonah Gabry (2015). Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models.
  • New covariance functions

    • gpcf_additive creates a mixture over products of kernels for each dimension reference: Duvenaud, D. K., Nickisch, H., & Rasmussen, C. E. (2011). Additive Gaussian processes. In Advances in neural information processing systems, pp. 226-234.
    • gpcf_linearLogistic corresponds to logistic mean function
    • gpcf_linearMichelismenten correpsonds Michelis Menten mean function

Improvements - faster EP moment calculation for lik_logit

Several minor bugfixes


Logo NaN toolbox 2.8.1

by schloegl - July 6, 2015, 22:43:23 CET [ Project Homepage BibTeX Download ] 35988 views, 7338 downloads, 3 subscriptions

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes:

Changes in v.2.8.1 - number of bug fixes - compatibility issues with recent versions of Octave are addressed - upgrade to libsvm 3-12

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.6

by hn - July 6, 2015, 12:31:28 CET [ Project Homepage BibTeX Download ] 25875 views, 5999 downloads, 4 subscriptions

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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Changes:
  • added a new inference function infGrid_Laplace allowing to use non-Gaussian likelihoods for large grids

  • fixed a bug due to Octave evaluating norm([]) to a tiny nonzero value, modified all lik/lik*.m functions reported by Philipp Richter

  • small bugfixes in covGrid and infGrid

  • bugfix in predictive variance of likNegBinom due to Seth Flaxman

  • bugfix in infFITC_Laplace as suggested by Wu Lin

  • bugfix in covPP{iso,ard}


Logo Simple Generalized Learning Vector Quantization 1.0

by fmschleif - June 4, 2015, 10:49:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 764 views, 171 downloads, 2 subscriptions

About: Simple and hopefully clean and easy to follow implementation of the Generalized Learning Vector Quantizer (GLVQ) with variants for metric adaptation (RGLVQ, GMLVQ, LiRaM).

Changes:

Initial Announcement on mloss.org.


Logo JMLR dlib ml 18.16

by davis685 - June 4, 2015, 04:50:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 106340 views, 17942 downloads, 4 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds a tool for solving linear model predictive control problems as well as improved python bindings and other usability improvements.


Logo Probabilistic Classification Vector Machine 0.21

by fmschleif - May 26, 2015, 16:24:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1546 views, 335 downloads, 3 subscriptions

About: PCVM library a c++/armadillo implementation of the Probabilistic Classification Vector Machine.

Changes:

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)


Logo KeBABS 1.2.3

by UBod - May 26, 2015, 10:55:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5105 views, 901 downloads, 3 subscriptions

About: Kernel-Based Analysis of Biological Sequences

Changes:
  • new export kebabsCollectInfo for collection of package info
  • update of version dependency to Biostrings, XVector, S4Vector
  • correction for leading + or - in factor label
  • change of bibtex style sheet in vignette to plainnat.bst

Logo Cognitive Foundry 3.4.1

by Baz - May 13, 2015, 06:55:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20724 views, 3421 downloads, 3 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

Changes:
  • General:
    • Updated MTJ to version 1.0.2 and netlib-java to 1.1.2.
    • Updated XStream to version 1.4.8.
  • Common:
    • Fixed issue in VectorUnionIterator.
  • Learning:
    • Added Alternating Least Squares (ALS) Factorization Machine training implementation.
    • Fixed performance issue in Factorization Machine where linear component was not making use of sparsity.
    • Added utility function to sigmoid unit.

Logo java machine learning platform 1.0

by openpr_nlpr - April 2, 2015, 09:02:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 886 views, 147 downloads, 2 subscriptions

About: Jmlp is a java platform for both of the machine learning experiments and application. I have tested it on the window platform. But it should be applicable in the linux platform due to the cross-platform of Java language. It contains the classical classification algorithm (Discrete AdaBoost.MH, Real AdaBoost.MH, SVM, KNN, MCE,MLP,NB) and feature reduction(KPCA,PCA,Whiten) etc.

Changes:

Initial Announcement on mloss.org.


Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6151 views, 1009 downloads, 3 subscriptions

About: Hivemall is a scalable machine learning library running on Hive/Hadoop.

Changes:
  • Supported Matrix Factorization
  • Added a support for TF-IDF computation
  • Supported AdaGrad/AdaDelta
  • Supported AdaGradRDA classification
  • Added normalization scheme

Logo JMLR Mulan 1.5.0

by lefman - February 23, 2015, 21:19:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18115 views, 6848 downloads, 2 subscriptions

About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.

Changes:

Learners

  • MLCSSP.java: Added the MLCSSP algorithm (from ICML 2013)
  • Enhancements of multi-target regression capabilities
  • Improved CLUS support
  • Added pairwise classifier and pairwise transformation

Measures/Evaluation

  • Providing training data in the Evaluator is unnecessary in the case of specific measures.
  • Examples with missing ground truth are not skipped for measures that handle missing values.
  • Added logistics and squared error losses and measures

Bug fixes

  • IndexOutOfBounds in calculation of MiAP and GMiAP
  • Bug fix in Rcut.java
  • When in rank/score mode the meta-data contained additional unecessary attributes. (Newton Spolaor)

API changes

  • Upgrade to Java 7
  • Upgrade to Weka 3.7.10

Miscalleneous

  • Small changes and improvements in the wrapper classes for the CLUS library
  • ENTCS13FeatureSelection.java (new experiment)
  • Enumeration is now used for specifying the type of meta-data. (Newton Spolaor)

Logo Hub Miner 1.1

by nenadtomasev - January 22, 2015, 16:33:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1894 views, 395 downloads, 2 subscriptions

About: Hubness-aware Machine Learning for High-dimensional Data

Changes:
  • BibTex support for all algorithm implementations, making all of them easy to reference (via algref package).

  • Two more hubness-aware approaches (meta-metric-learning and feature construction)

  • An implementation of Hit-Miss networks for analysis.

  • Several minor bug fixes.

  • The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF.

  • The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k.

  • Some more experimental scripts have been included.

  • Extensions in the estimation of hubness risk.

  • Alias and weighted reservoir methods for weight-proportional random selection.


Logo pyGPs 1.3.2

by mn - January 17, 2015, 13:08:43 CET [ Project Homepage BibTeX Download ] 4930 views, 1177 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 WEKA 3.7.12

by mhall - December 17, 2014, 03:04:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 47096 views, 6971 downloads, 3 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

In core weka:

  • GUIChooser now has a plugin exension point that allows implementations of GUIChooser.GUIChooserMenuPlugin to appear as entries in either the Tools or Visualization menus
  • SubsetByExpression filter now has support for regexp matching
  • weka.classifiers.IterativeClassifierOptimizer - a classifier that can efficiently optimize the number of iterations for a base classifier that implements IterativeClassifier
  • Speedup for LogitBoost in the two class case
  • weka.filters.supervised.instance.ClassBalancer - a simple filter to balance the weight of classes
  • New class hierarchy for stopwords algorithms. Includes new methods to read custom stopwords from a file and apply multiple stopwords algorithms
  • Ability to turn off capabilities checking in Weka algorithms. Improves runtime for ensemble methods that create a lot of simple base classifiers
  • Memory savings in weka.core.Attribute
  • Improvements in runtime for SimpleKMeans and EM
  • weka.estimators.UnivariateMixtureEstimator - new mixture estimator

In packages:

  • New discriminantAnalysis package. Provides an implementation of Fisher's linear discriminant analysis
  • Quartile estimators, correlation matrix heat map and k-means++ clustering in distributed Weka
  • Support for default settings for GridSearch via a properties file
  • Improvements in scripting with addition of the offical Groovy console (kfGroovy package) from the Groovy project and TigerJython (new tigerjython package) as the Jython console via the GUIChooser
  • Support for the latest version of MLR in the RPlugin package
  • EAR4 package contributed by Vahid Jalali
  • StudentFilters package contributed by Chris Gearhart
  • graphgram package contributed by Johannes Schneider

Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3415 views, 727 downloads, 1 subscription

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

Changes:

improved testing, improved documentation, windows compatibility, more algorithms


Logo AugmentedSVM 1.0.0

by ashukla - October 2, 2014, 11:24:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1466 views, 316 downloads, 2 subscriptions

About: A MATLAB toolkit for performing generalized regression with equality/inequality constraints on the function value/gradient.

Changes:

Initial Announcement on mloss.org.


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 4230 views, 1278 downloads, 3 subscriptions

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

Changes:
  • 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

Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29328 views, 8232 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


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