Projects supporting the csv data format.
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Logo Ordinal Choquistic Regression 1.00

by AliFall - January 30, 2014, 15:42:34 CET [ BibTeX BibTeX for corresponding Paper Download ] 1080 views, 254 downloads, 1 subscription

About: "Ordinal Choquistic Regression" model using the maximum likelihood

Changes:

Initial Announcement on mloss.org.


Logo ELKI 0.6.0

by erich - January 10, 2014, 18:32:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10854 views, 1942 downloads, 3 subscriptions

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 0.5.5:

Algorithms

Clustering:

  • Hierarchical Clustering - the slower naive variants were added, and the code was refactored
  • Partition extraction from hierarchical clusterings - different linkage strategies (e.g. Ward)
  • Canopy pre-Clustering
  • Naive Mean-Shift Clustering
  • Affinity propagation clustering (both with distances and similarities / kernel functions)
  • K-means variations: Best-of-multiple-runs, bisecting k-means
  • New k-means initialization: farthest points, sample initialization
  • Cheng and Church Biclustering
  • P3C Subspace Clustering
  • One-dimensional clustering algorithm based on kernel density estimation

Outlier detection

  • COP - correlation outlier probabilities
  • LDF - a kernel density based LOF variant
  • Simplified LOF - a simpler version of LOF (not using reachability distance)
  • Simple Kernel Density LOF - a simple LOF using kernel density (more consistent than LDF)
  • Simple outlier ensemble algorithm
  • PINN - projection indexed nearest neighbors, via projected indexes.
  • ODIN - kNN graph based outlier detection
  • DWOF - Dynamic-Window Outlier Factor (contributed by Omar Yousry)
  • ABOD refactored, into ABOD, FastABOD and LBABOD

Distances

  • Geodetic distances now support different world models (WGS84 etc.) and are subtantially faster.
  • Levenshtein distances for processing strings, e.g. for analyzing phonemes (contributed code, see "Word segmentation through cross-lingual word-to-phoneme alignment", SLT2013, Stahlberg et al.)
  • Bray-Curtis, Clark, Kulczynski1 and Lorentzian distances with R-tree indexing support
  • Histogram matching distances
  • Probabilistic divergence distances (Jeffrey, Jensen-Shannon, Chi2, Kullback-Leibler)
  • Kulczynski2 similarity
  • Kernel similarity code has been refactored, and additional kernel functions have been added

Database Layer and Data Types

Projection layer * Parser for simple textual data (for use with Levenshtein distance) Various random projection families (including Feature Bagging, Achlioptas, and p-stable) Latitude+Longitude to ECEF Sparse vector improvements and bug fixes New filter: remove NaN values and missing values New filter: add histogram-based jitter New filter: normalize using statistical distributions New filter: robust standardization using Median and MAD New filter: Linear discriminant analysis (LDA)

Index Layer

  • Another speed up in R-trees
  • Refactoring of M- and R-trees: Support for different strategies in M-tree New strategies for M-tree splits Speedups in M-tree
  • New index structure: in-memory k-d-tree
  • New index structure: in-memory Locality Sensitive Hashing (LSH)
  • New index structure: approximate projected indexes, such as PINN
  • Index support for geodetic data - (Details: Geodetic Distance Queries on R-Trees for Indexing Geographic Data, SSTD13)
  • Sampled k nearest neighbors: reference KDD13 "Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles"
  • Cached (precomputed) k-nearest neighbors to share across multiple runs
  • Benchmarking "algorithms" for indexes

Mathematics and Statistics

  • Many new distributions have been added, now 28 different distributions are supported
  • Additional estimation methods (using advanced statistics such as L-Moments), now 44 estimators are available
  • Trimming and Winsorizing
  • Automatic best-fit distribution estimation
  • Preprocessor using these distributions for rescaling data sets
  • API changes related to the new distributions support
  • More kernel density functions
  • RANSAC covariance matrix builder (unfortunately rather slow)

Visualization

  • 3D projected coordinates (Details: Interactive Data Mining with 3D-Parallel-Coordinate-Trees, SIGMOD2013)
  • Convex hulls now also include nested hierarchical clusters

Other

  • Parser speedups
  • Sparse vector bug fixes and improvements
  • Various bug fixes
  • PCA, MDS and LDA filters
  • Text output was slightly improved (but still needs to be redesigned from scratch - please contribute!)
  • Refactoring of hierarchy classes
  • New heap classes and infrastructure enhancements
  • Classes can have aliases, e.g. "l2" for euclidean distance.
  • Some error messages were made more informative.
  • Benchmarking classes, also for approximate nearest neighbor search.

Logo Gesture Recogition Toolkit 0.1 Revision 289

by ngillian - December 13, 2013, 22:59:53 CET [ Project Homepage BibTeX Download ] 3773 views, 691 downloads, 1 subscription

About: The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library that has been specifically designed for real-time gesture recognition. It features a large number of machine-learning algorithms for both classification and regression in addition to a wide range of supporting algorithms for pre-processing, feature extraction and dataset management. The GRT has been designed for real-time gesture recognition, but it can also be applied to more general machine-learning tasks.

Changes:

Added Decision Tree and Random Forests.


Logo Differential Dependency Network cabig cytoscape plugin 1.0

by cbil - October 27, 2013, 17:31:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1420 views, 313 downloads, 1 subscription

About: DDN learns and visualize differential dependency networks from condition-specific data.

Changes:

Initial Announcement on mloss.org.


Logo Hivemall 0.1

by myui - October 25, 2013, 08:43:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3807 views, 589 downloads, 1 subscription

About: Hivemall is a scalable machine learning library running on Hive/Hadoop, licensed under the LGPL 2.1.

Changes:
  • Enhancement

    • Added AROW regression
    • Added AROW with a hinge loss (arowh_regress())
  • Bugfix

    • Fixed a bug of null feature handling in classification/regression

Logo JMLR CAM Java 3.1

by wangny - October 14, 2013, 22:46:03 CET [ Project Homepage BibTeX Download ] 5996 views, 2751 downloads, 1 subscription

About: The CAM R-Java software provides a noval way to solve blind source separation problem.

Changes:

In this version, we fix the problem of not working under newest R version R-3.0.


Logo MyMediaLite 3.10

by zenog - October 8, 2013, 22:29:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 44705 views, 8445 downloads, 1 subscription

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


Logo NuPIC 0.1

by rhyolight - August 21, 2013, 21:01:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1251 views, 493 downloads, 1 subscription

About: The mission of this project is to build and support a community interested in machine learning and machine intelligence based on modeling the neocortex and the principles upon which it works.

Changes:

Initial Announcement on mloss.org.


Logo Cognitive Foundry 3.3.3

by Baz - May 21, 2013, 05:59:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16979 views, 2691 downloads, 2 subscriptions

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

Changes:
  • General:
    • Made code able to compile under both Java 1.6 and 1.7. This required removing some potentially unsafe methods that used varargs with generics.
    • Upgraded XStream dependency to 1.4.4.
    • Improved support for regression algorithms in learning.
    • Added general-purpose adapters to make it easier to compose learning algorithms and adapt their input or output.
  • Common Core:
    • Added isSparse, toArray, dotDivide, and dotDivideEquals methods for Vector and Matrix.
    • Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to Ring (and thus Vector and Matrix) for potentially faster such operations.
    • Fixed issue where matrix and dense vector equals was not checking for equal dimensionality.
    • Added transform, transformEquals, tranformNonZeros, and transformNonZerosEquals to Vector.
    • Made LogNumber into a signed version of a log number and moved the prior unsigned implementation into UnsignedLogNumber.
    • Added EuclideanRing interface that provides methods for times, timesEquals, divide, and divideEquals. Also added Field interface that provides methods for inverse and inverseEquals. These interfaces are now implemented by the appropriate number classes such as ComplexNumber, MutableInteger, MutableLong, MutableDouble, LogNumber, and UnsignedLogNumber.
    • Added interface for Indexer and DefaultIndexer implementation for creating a zero-based indexing of values.
    • Added interfaces for MatrixFactoryContainer and DivergenceFunctionContainer.
    • Added ReversibleEvaluator, which various identity functions implement as well as a new utility class ForwardReverseEvaluatorPair to create a reversible evaluator from a pair of other evaluators.
    • Added method to create an ArrayList from a pair of values in CollectionUtil.
    • ArgumentChecker now properly throws assertion errors for NaN values. Also added checks for long types.
    • Fixed handling of Infinity in subtraction for LogMath.
    • Fixed issue with angle method that would cause a NaN if cosine had a rounding error.
    • Added new createMatrix methods to MatrixFactory that initializes the Matrix with the given value.
    • Added copy, reverse, and isEmpty methods for several array types to ArrayUtil.
    • Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or LinkedHashSet with an expected size to CollectionUtil.
    • Added getFirst and getLast methods for List types to CollectionUtil.
    • Removed some calls to System.out and Exception.printStackTrace.
  • Common Data:
    • Added create method for IdentityDataConverter.
    • ReversibleDataConverter now is an extension of ReversibleEvaluator.
  • Learning Core:
    • Added general learner transformation capability to make it easier to adapt and compose algorithms. InputOutputTransformedBatchLearner provides this capability for supervised learning algorithms by composing together a triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
    • Added a constant and identity learners.
    • Added Chebyshev, Identity, and Minkowski distance metrics.
    • Added methods to DatasetUtil to get the output values for a dataset and to compute the sum of weights.
    • Made generics more permissive for supervised cost functions.
    • Added ClusterDistanceEvaluator for taking a clustering that encodes the distance from an input value to all clusters and returns the result as a vector.
    • Fixed potential round-off issue in decision tree splitter.
    • Added random subspace technique, implemented in RandomSubspace.
    • Separated functionality from LinearFunction into IdentityScalarFunction. LinearFunction by default is the same, but has parameters that can change the slope and offset of the function.
    • Default squashing function for GeneralizedLinearModel and DifferentiableGeneralizedLinearModel is now a linear function instead of an atan function.
    • Added a weighted estimator for the Poisson distribution.
    • Added Regressor interface for evaluators that are the output of (single-output) regression learning algorithms. Existing such evaluators have been updated to implement this interface.
    • Added support for regression ensembles including additive and averaging ensembles with and without weights. Added a learner for regression bagging in BaggingRegressionLearner.
    • Added a simple univariate regression class in UnivariateLinearRegression.
    • MultivariateDecorrelator now is a VectorInputEvaluator and VectorOutputEvaluator.
    • Added bias term to PrimalEstimatedSubGradient.
  • Text Core:
    • Fixed issue with the start position for tokens from LetterNumberTokenizer being off by one except for the first one.

Logo HDDM 0.5

by Wiecki - April 24, 2013, 02:53:07 CET [ Project Homepage BibTeX Download ] 3584 views, 900 downloads, 1 subscription

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.

Changes:
  • New and improved HDDM model with the following changes:
    • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
    • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
    • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
    • The old model has been renamed to HDDMTransformed.
    • HDDMRegression and HDDMStimCoding are also using this model.
  • HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
  • Improved online documentation at http://ski.clps.brown.edu/hddm_docs
  • A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
  • Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
  • Maximum likelihood optimization.
  • Many bugfixes and better test coverage.
  • hddm_fit.py command line utility is depracated.

Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 19005 views, 4476 downloads, 2 subscriptions

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

Changes:

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)


Logo Neural network designer 1.1.1

by bragi - December 28, 2012, 11:38:10 CET [ Project Homepage BibTeX Download ] 2851 views, 806 downloads, 1 subscription

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


Logo Divvy 1.1.1

by jlewis - November 14, 2012, 20:21:29 CET [ Project Homepage BibTeX Download ] 1783 views, 895 downloads, 1 subscription

About: Divvy is a Mac OS X application for performing dimensionality reduction, clustering, and visualization.

Changes:

Initial Announcement on mloss.org.


Logo Pattern 2.4

by tomdesmedt - August 31, 2012, 02:26:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7032 views, 1845 downloads, 1 subscription

About: "Pattern" is a web mining module for Python. It bundles tools for data retrieval, text analysis, clustering and classification, and data visualization.

Changes:
  • Small bug fixes in overall + performance improvements.
  • Module pattern.web: updated to the new Bing API (Bing API has is paid service now).
  • Module pattern.en: now includes Norvig's spell checking algorithm.
  • Module pattern.de: new German tagger/chunker, courtesy of Schneider & Volk (1998) who kindly agreed to release their work in Pattern under BSD.
  • Module pattern.search: the search syntax now includes { } syntax to define match groups.
  • Module pattern.vector: fast implementation of information gain for feature selection.
  • Module pattern.graph: now includes a toy semantic network of commonsense (see examples).
  • Module canvas.js: image pixel effects & editor now supports live editing

Logo MLWizard 5.2

by remat - July 26, 2012, 15:04:14 CET [ Project Homepage BibTeX Download ] 2945 views, 740 downloads, 1 subscription

About: MLwizard recommends and optimizes classification algorithms based on meta-learning and is a software wizard fully integrated into RapidMiner but can be used as library as well.

Changes:

Faster parameter optimization using genetic algorithm with predefined start population.


Logo NaN toolbox 2.5.2

by schloegl - February 10, 2012, 11:45:52 CET [ Project Homepage BibTeX Download ] 30406 views, 6169 downloads, 1 subscription

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

Changes:

Changes in v.2.5.2 - faster version of quantile if multiple quantiles are requested - removes the dependency on ZLIB and thus - fixes "pkg install nan" for Octave on Windows - a number of minor improvements

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


Logo JMLR SSA Toolbox 1.3

by paulbuenau - January 24, 2012, 15:51:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12429 views, 3767 downloads, 1 subscription

About: The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex high-dimensional data sets.

Changes:
  • Various bugfixes.

Logo mldata-utils 0.5.0

by sonne - April 8, 2011, 10:02:44 CET [ Project Homepage BibTeX Download ] 20071 views, 4202 downloads, 1 subscription

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

Changes:
  • Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
  • Various bugfixes.

Logo Figue 1.0.1

by lerhumcbon - August 17, 2010, 04:03:35 CET [ Project Homepage BibTeX Download ] 3313 views, 788 downloads, 1 subscription

About: A collection of clustering algorithms implemented in Javascript.

Changes:

Initial Announcement on mloss.org.


Logo ELF Ensemble Learning Framework 0.1

by mjahrer - May 10, 2010, 23:54:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4966 views, 822 downloads, 1 subscription

About: ELF provides many well implemented supervised learners for classification and regression tasks with an opportunity of ensemble learning.

Changes:

Initial Announcement on mloss.org.


Showing Items 21-40 of 40 on page 2 of 2: Previous 1 2