Projects that are tagged with online learning.
Showing Items 1-20 of 26 on page 1 of 2: 1 2 Next

Logo KeLP 2.0.0

by kelpadmin - November 26, 2015, 16:14:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3838 views, 966 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.


This is a major release that includes brand new features as well as a renewed architecture of the entire project.

Now KeLP is organized in four maven projects:

  • kelp-core: it contains the infrastructure of abstract classes and interfaces to work with KeLP. Furthermore, some implementations of algorithms, kernels and representations are included, to provide a base operative environment.

  • kelp-additional-kernels: it contains several kernel functions that extend the set of kernels made available in the kelp-core project. Moreover, this project implements the specific representations required to enable the application of such kernels. In this project the following kernel functions are considered: Sequence kernels, Tree kernels and Graphs kernels.

  • kelp-additional-algorithms: it contains several learning algorithms extending the set of algorithms provided in the kelp-core project, e.g. the C-Support Vector Machine or ν-Support Vector Machine learning algorithms. In particular, advanced learning algorithms for classification and regression can be found in this package. The algorithms are grouped in: 1) Batch Learning, where the complete training dataset is supposed to be entirely available during the learning phase; 2) Online Learning, where individual examples are exploited one at a time to incrementally acquire the model.

  • kelp-full: this is the complete package of KeLP. It aggregates the previous modules in one jar. It contains also a set of fully functioning examples showing how to implement a learning system with KeLP. Batch learning algorithm as well as Online Learning algorithms usage is shown here. Different examples cover the usage of standard kernel, Tree Kernels and Sequence Kernel, with caching mechanisms.

Furthermore this new release includes:

  • CsvDatasetReader: it allows to read files in CSV format

  • DCDLearningAlgorithm: it is the implementation of the Dual Coordinate Descent learning algorithm

  • methods for checking the consistency of a dataset.

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 2.0.0!

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 530 views, 82 downloads, 1 subscription

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.


Initial Announcement on

Logo ABACOC Adaptive Ball Cover for Classification 2.0

by kikot - May 29, 2015, 11:57:28 CET [ BibTeX BibTeX for corresponding Paper Download ] 3077 views, 780 downloads, 3 subscriptions

About: Incremental (Online) Nonparametric Classifier. You can classify both points (standard) or matrices (multivariate time series). Java and Matlab code already available.


version 2: parameterless system, constant model size, prediction confidence (for active learning).

NEW!! C++ version at:

Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7379 views, 1233 downloads, 3 subscriptions

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

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

Logo QSMM 1.16

by olegvol - July 29, 2014, 19:37:31 CET [ Project Homepage BibTeX Download ] 1418 views, 381 downloads, 3 subscriptions

About: The implementation of adaptive probabilistic mappings.


Initial Announcement on

Logo Kernel Adaptive Filtering Toolbox 1.4

by steven2358 - May 26, 2014, 18:24:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5961 views, 1065 downloads, 1 subscription

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

  • Improvements and demo script for profiler
  • Initial version of documentation
  • Several new algorithms

Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15059 views, 5703 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.


New version November 2013

Logo LIBOL 0.3.0

by stevenhoi - December 12, 2013, 15:26:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12793 views, 4573 downloads, 2 subscriptions

About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification.


In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows:

• Add a template and guide for adding new algorithms;

• Improve parameter settings and make documentation clear;

• Improve documentation on data formats and key functions;

• Amend the "OGD" function to use different loss types;

• Fixed some name inconsistency and other minor bugs.

Logo Jubatus 0.5.0

by hido - November 30, 2013, 17:41:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3704 views, 713 downloads, 1 subscription

About: Jubatus is a general framework library for online and distributed machine learning. It currently supports classification, regression, clustering, recommendation, nearest neighbors, anomaly detection, and graph analysis. Loose model sharing provides higher scalability, better performance, and real-time capabilities, by combining online learning with distributed computations.


0.5.0 add new supports for clustering and nearest neighbors. For more detail, see

Logo pymaBandits 1.0

by garivier - July 6, 2012, 18:32:41 CET [ BibTeX Download ] 10254 views, 1805 downloads, 1 subscription

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

Logo JMLR LWPR 1.2.4

by sklanke - February 6, 2012, 19:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 31578 views, 3951 downloads, 1 subscription

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...]


Version 1.2.4

  • Corrected typo in lwpr.c (wrong function name for multi-threaded helper function on Unix systems) Thanks to Jose Luis Rivero

Logo OpenViBE 0.8.0

by k3rl0u4rn - October 1, 2010, 16:15:08 CET [ Project Homepage BibTeX Download ] 12751 views, 3539 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarEmpty StarEmpty Star
(based on 1 vote)

About: OpenViBE is an opensource platform that enables to design, test and use Brain-Computer Interfaces (BCI). Broadly speaking, OpenViBE can be used in many real-time Neuroscience applications [...]


New release 0.8.0.

Logo sofia ml 0.1

by dsculley - December 29, 2009, 23:30:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6027 views, 1072 downloads, 0 comments, 1 subscription

About: A fast implementation of several stochastic gradient descent learners for classification, ranking, and ROC area optimization, suitable for large, sparse data sets. Includes Pegasos SVM, SGD-SVM, Passive-Aggressive Perceptron, Perceptron with Margins, Logistic Regression, and ROMMA. Commandline utility and API libraries are provided.


Initial Announcement on

Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 19596 views, 8074 downloads, 2 subscriptions

Rating Whole StarWhole Star1/2 StarEmpty StarEmpty Star
(based on 2 votes)

About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]


This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.

Logo Online Random Forests 0.11

by amirsaffari - October 3, 2009, 17:25:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9573 views, 1727 downloads, 1 subscription

About: This package implements the “Online Random Forests” (ORF) algorithm of Saffari et al., ICCV-OLCV 2009. This algorithm extends the offline Random Forests (RF) to learn from online training data samples. ORF is a multi-class classifier which is able to learn the classifier without 1-vs-all or 1-vs-1 binary decompositions.


Initial Announcement on

Logo LASVM 1.1

by leonbottou - August 3, 2009, 15:50:30 CET [ Project Homepage BibTeX Download ] 10037 views, 1857 downloads, 0 subscriptions

About: Reference implementation of the LASVM online and active SVM algorithms as described in the JMLR paper. The interesting bit is a small C library that implements the LASVM process and reprocess [...]


Minor bug fix

Logo Debellor 1.0

by mwojnars - July 30, 2009, 16:48:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8444 views, 2315 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.

  • 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
  • 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 OLaRankGreedy 1.0

by antojne - June 24, 2009, 17:07:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5040 views, 1081 downloads, 1 subscription

About: OLaRankGreedy is an online solver of the dual formulation of support vector machines for sequence labeling using greedy inference.


Initial Announcement on

Logo OLaRankExact 1.0

by antojne - June 24, 2009, 17:03:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4700 views, 1123 downloads, 1 subscription

About: OLaRank is an online solver of the dual formulation of support vector machines for sequence labeling using viterbi decoding.


Initial Announcement on

Logo Piqle 2.0

by fdecomite - June 19, 2009, 10:16:53 CET [ Project Homepage BibTeX Download ] 4132 views, 1985 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.


Initial Announcement on

Showing Items 1-20 of 26 on page 1 of 2: 1 2 Next