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Logo SVM and Kernel Methods Toolbox 0.5

by arakotom - June 10, 2008, 21:29:39 CET [ Project Homepage BibTeX Download ] 9912 views, 2359 downloads, 2 subscriptions

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About: SVM Toolbox fully written in Matlab (even the QP solver). Features : SVM, MultiClassSVM, One-Class, SV Regression, AUC-SVM and Rankboost, 1-norm SVM, Regularization Networks, Kernel Basis Pursuit [...]

Changes:

Initial Announcement on mloss.org.


Logo Lush 1.2.1

by ylecun - November 12, 2007, 06:35:08 CET [ Project Homepage BibTeX Download ] 5374 views, 2357 downloads, 0 subscriptions

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About: Lush is an object-oriented Lisp dialect with a super-simple way of integrating C/C++ code and libraries. It includes extensive libraries for numerical computing, machine learning, and computer [...]

Changes:

Initial Announcement on mloss.org.


Logo JMLR Tapkee 1.0

by blackburn - April 10, 2014, 02:45:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7938 views, 2356 downloads, 1 subscription

About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction.

Changes:

Initial Announcement on mloss.org.


Logo PREA Personalized Recommendation Algorithms Toolkit 1.1

by srcw - September 1, 2012, 22:53:37 CET [ Project Homepage BibTeX Download ] 9153 views, 2329 downloads, 2 subscriptions

About: An open source Java software providing collaborative filtering algorithms.

Changes:

Initial Announcement on mloss.org.


Logo JMLR pebl Python Environment for Bayesian Learning 1.0.1

by abhik - March 5, 2009, 00:05:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23618 views, 2328 downloads, 1 subscription

About: Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations.

Changes:

Updated version to 1.0.1


Logo LIBSVM 2.9

by cjlin - February 27, 2010, 01:09:23 CET [ Project Homepage BibTeX Download ] 11272 views, 2317 downloads, 1 subscription

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About: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class [...]

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 ] 12665 views, 2310 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 PyML a python machine learning library focused on kernel methods 0.7.0

by asa - May 29, 2008, 22:23:39 CET [ Project Homepage BibTeX Download ] 8943 views, 2301 downloads, 0 comments, 0 subscriptions

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About: PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods.

Changes:

Initial Announcement on mloss.org.


About: TinyOS is a small operating for small (wireless) sensors. LEGO MINDSTORMS NXT is a platform for embedded systems experimentation: The combination of NXT and TinyOS is NXTMOTE.

Changes:

Initial Announcement on mloss.org.


Logo monte python 0.1.0

by roro - May 9, 2008, 21:45:47 CET [ Project Homepage BibTeX Download ] 5774 views, 2274 downloads, 1 subscription

About: Monte (python) is a small machine learning library written in pure Python. The focus is on gradient based learning, in particular on the construction of complex models from many smaller components.

Changes:

Initial Announcement on mloss.org.


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