GeezerVison is showing mloss.ratschlab.org — mloss | Projects that are tagged with algorithms. ARCHIVE Snapshot of mloss.org – machine learning open source software as of 2025: preserved, not maintained. Kept by ETH Zurich · Legal notice & privacy
Projects that are tagged with algorithms.
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- MLOSS workshop at ICML 2015: Open Ecosystems
by
Antti Honkela
on March 30, 2015
- A third of the top 100 papers are about software
by
Cheng Soon Ong
on October 30, 2014
- Open Machine Learning Workshop
by
Cheng Soon Ong
on July 28, 2014
- Machine Learning Distro
by
Cheng Soon Ong
on July 22, 2014
- Google Summer of Code 2014
by
Cheng Soon Ong
on June 3, 2014
- Reproducibility is not simple
by
Cheng Soon Ong
on March 30, 2014
- Keynotes at ACML 2013
by
Cheng Soon Ong
on November 14, 2013
- What does the “OSS” in MLOSS mean?
by
Mark Reid
on September 1, 2013
- Code review for science
by
Cheng Soon Ong
on August 14, 2013
- GSoC 2013
by
Cheng Soon Ong
on April 9, 2013
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Showing Items 1-20 of 21 on page 1 of 2:
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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 bunch of new image processing routines as well as many minor usability improvements and bug fixes.
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- Operating System:
Linux,
Macosx,
Windows,
Unix,
Solaris
- Data Formats:
Svmlight,
Binary,
Csv
- JMLR-MLOSS Publication:
JMLR Page
- Tags:
Svm,
Classification,
Clustering,
Regression,
Kernel Methods,
Matrix Library,
Kkmeans,
Optimization,
Algorithms,
Exact Bayesian Methods,
Approximate Inference,
Bayesian Networks,
Junction Tree
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About:
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.
Changes:
For a complete list of changes, please see the full release notes at the release details page at:
https://github.com/accord-net/framework/releases/tag/v3.8.0
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- Operating System:
Linux,
Windows,
Platform Independent
- Data Formats:
Matlab,
Agnostic,
Numpy,
Various,
Excel,
Mat,
Libsvm Format
- Tags:
Svm,
Kernel Methods,
Algorithms,
Classifiers,
Statistics,
Clustering Algorithm,
Probability Estimation,
Discriminant Analysis,
Wavelet Transform,
Principal Component Analysis,
Algebra,
Fourier,
Cluste
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About:
ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.
Changes:
Additions and improvements from ELKI 0.7.0 to 0.7.1:
Algorithm additions:
GriDBSCAN: DBSCAN using grid partitioning (Minkowski distances only)
Compare-Means and Sort-Means k-means variations (much faster than traditional k-means)
Visualization of dendrograms.
Important bug fixes:
Classes with no package ("default package") would cause errors.
The fast power function implementation was sometimes returning incorrect results.
Random sampling was sometimes not sampling from the full data set.
UI improvements:
The file input source will now automatically choose the Arff parser for .arff files.
MiniGUI now allows choosing other applications.
MiniGUI now displays the command line in a separate field.
MiniGUI displays an error message, if an incorrect classpath or JAyatana (on Ubuntu) is detected.
Export to png now works, we added a work-around for an open Batik bug.
Smaller changes:
Many smaller bug fixes.
C-Index for cluster evaluation now can process larger data sets.
OPTICS output of undefined reachability fixed.
External distance matrixes are easier to use and perform additional checks.
Precomputed distance matrixes can answer range and kNN queries.
Voronoi visualization can be switched in the menu now.
Improved backwards command line compatibility with additional aliases.
Added generated @since annotations in JavaDoc.
Many new unit tests, renamed to the Java conventions.
Low-level reading of service files, to have faster startup.
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About:
The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.
Changes:
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General:
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Common:
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Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
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Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
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Optimized DenseVector by removing a layer of indirection.
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Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
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Added utility class for enumerating combinations.
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Adjusted ScalarMap implementation hierarchy.
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Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
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Added method for creating square identity matrix to MatrixFactory.
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Added Random implementation that uses a cached set of values.
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Learning:
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Implemented feature hashing.
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Added factory for random forests.
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Implemented uniform distribution over integer values.
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Added Chi-squared similarity.
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Added KL divergence.
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Added general conditional probability distribution.
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Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
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Fixed null pointer exception that can happen in K-means with an empty cluster.
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Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
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Text:
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Improvements to LDA Gibbs sampler.
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- Operating System:
Agnostic,
Platform Independent
- Data Formats:
Matlab,
Csv,
Xml,
Xstream
- Tags:
Classification,
Clustering,
Adaboost,
Decision Tree Learning,
Algorithms,
Gaussian Mixture Models,
Bagging,
Ensemble Methods,
Gaussian Processes,
Affinity Propagation,
Bfgs,
Generics,
Genetic Algorith
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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.
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- Operating System:
Agnostic
- Data Formats:
Ascii,
Any Format Supported By Matlab,
Libsvm Format,
Multiple Representations Format
- Tags:
Classification,
Clustering,
Regression,
Support Vector Machines,
Online Learning,
Stochastic Gradient Descent,
Machine Learning,
Optimization,
Algorithms,
Supervised Learning,
Sparse Learning,
Sparsity,
Regularization,
Prediction,
Online Multiclass Classification,
Sparse Data,
Statistical Learning,
Machine Learning Toolbox,
Stochastic Optimization
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About:
The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multi-threading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more.
Changes:
Updated to version 0.3.0
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- Operating System:
Platform Independent
- Data Formats:
Ascii,
Binary,
Matlab,
Java Arrays,
Lucene,
Txt,
Wav
- Tags:
Large Scale,
Matlab,
Octave,
Matrix Library,
High Dimensional Data,
Parallel,
Algorithms,
Pca,
Missing Data,
Lapack,
Linear Algebra,
Matrix,
Matrix Factorization,
Large Datasets,
Java,
Icml2010
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About:
MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.
Changes:
Mostly bug fixes.
For details see:
https://github.com/zenogantner/MyMediaLite/blob/master/doc/Changes
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- Operating System:
Linux,
Windows,
Solaris,
Mac Os X
- Data Formats:
Csv,
Tab Separated,
Sql
- Tags:
Gradient Based Learning,
Large Scale Learning,
Algorithms,
Data Mining,
Evaluation,
Supervised Learning,
Collaborative Filtering,
Matrix Factorization,
Recommender Systems,
Knn,
Library,
Dotnet,
Mono
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About:
a dbms for resonating neural networks. Create and use different types of machine learning algorithms.
Changes:
AIML compatible (AIML files can be imported); new 'Grid channel' for developing board games; improved topics editor; new demo project: ALice (from AIML); lots of bug-fixes and speed improvements
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- Operating System:
Windows
- Data Formats:
Binary,
Txt,
Csv,
Xml
- Tags:
Neural Nets,
Neural Networks,
Decision Trees,
Parallel,
Machine Learning,
Natural Language Processing,
Decision Tree Learning,
Algorithms,
Probabilistic Models,
Machine Translation,
Parsing,
Random Fo
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About:
An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.”
The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm
Changes:
Initial Announcement on mloss.org.
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About:
This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.
Changes:
Initial Announcement on mloss.org.
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- Operating System:
Agnostic
- Data Formats:
Any Format Supported By Matlab
- Tags:
Large Scale,
Kernelmachine,
Svm,
Bci,
Classification,
Support Vector Machines,
Feature Selection,
Linear Svm,
Convex Optimization,
Gradient Based Learning,
Manifold Learning,
Optimization,
Algorithms,
Feature Weighting,
Trace Norm,
Toolbox,
Group Lasso,
Lasso,
Sparse Learning,
Quadratic Programming,
Weighting,
L1 Regularization,
Large Datasets,
Regularization,
Pattern Recognition,
Discriminant Analysis,
Linear Model,
Generalized Linear Models,
Multiclass Support Vector Machine,
L1 Minimization,
Sparse Representation,
L1 Norm,
L21 Norm,
Dimension Reduction,
Multi Task
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About:
MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
Changes:
What's new in version 3.3?
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support sklearn versions up to 0.12
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cleanly support reload
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fail gracefully if pp server does not start
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several bug-fixes and improvements
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About:
Matlab SVM toolbox for learning large margin filters in signal or images.
Changes:
Initial Announcement on mloss.org.
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- Operating System:
Platform Independent
- Data Formats:
Matlab
- Tags:
Support Vector Machine,
Matlab,
Classification,
Feature Selection,
Algorithms,
Regularization,
Feature Extraction,
Data Generator,
Filtering,
Image Classification,
Linear Classifier
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About:
MATLAB toolbox for advanced Brain-Computer Interface (BCI) research.
Changes:
Initial Announcement on mloss.org.
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- Operating System:
Linux,
Macosx,
Windows
- Data Formats:
Biosignal Formats
- Tags:
Bioinformatics,
Sequence Analysis,
Matlab,
Bci,
Brain Computer Interface,
Electroencephalogram,
Classification,
Preprocessing,
Manifold Learning,
Algorithms,
Supervised Learning,
Toolbox,
Probabilistic Models,
Data Streams,
Feature Extraction,
Performance Measures,
Filtering
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About:
Tools for functional network analysis.
Changes:
Initial Announcement on mloss.org.
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- Operating System:
Agnostic
- Data Formats:
R
- Tags:
Bioinformatics,
R,
High Dimensional Data,
Machine Learning,
Algorithms,
Data Mining,
Microarrays,
Bioconductor,
Variational Inference,
Gaussian Mixture Model,
Mixture Models,
Probabilistic Models,
Probability Estimation,
Biclustering,
Gene Expression
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About:
yaplf (Yet Another Python Learning Framework) is an extensible machine learning framework written in python
Changes:
Initial Announcement on mloss.org.
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About:
Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists.
Changes:
new release
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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.
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About:
Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.
Changes:
Initial Announcement on mloss.org.
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About:
LaRank is an online solver for multiclass Support Vector Machines.
Changes:
Initial Announcement on mloss.org.
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- Operating System:
Linux,
Macosx,
Unix
- Data Formats:
None
- Tags:
Svm,
Classification,
Support Vector Machines,
Online Learning,
Kernel Methods,
Multi Class,
Gradient Based Learning,
Large Scale Learning,
Machine Learning,
Algorithms
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About:
Nested Effects Models (NEMs) are a class of directed graphical models originally introduced to analyze the effects of gene perturbation screens with high-dimensional phenotypes. In contrast to other [...]
Changes:
Initial Announcement on mloss.org.
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Showing Items 1-20 of 21 on page 1 of 2:
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