All entries.
Showing Items 71-80 of 534 on page 8 of 54: First Previous 3 4 5 6 7 8 9 10 11 12 13 Next Last

Logo JMLR BudgetedSVM v1.1

by nemanja - February 12, 2014, 20:53:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 948 views, 169 downloads, 1 subscription

About: BudgetedSVM is an open-source C++ toolbox for scalable non-linear classification. The toolbox can be seen as a missing link between LibLinear and LibSVM, combining the efficiency of linear with the accuracy of kernel SVM. We provide an Application Programming Interface for efficient training and testing of non-linear classifiers, supported by data structures designed for handling data which cannot fit in memory. We also provide command-line and Matlab interfaces, providing users with an efficient, easy-to-use tool for large-scale non-linear classification.

Changes:

Initial Announcement on mloss.org.


Logo The Choquet Kernel 1.00

by AliFall - February 11, 2014, 16:21:15 CET [ BibTeX BibTeX for corresponding Paper Download ] 620 views, 154 downloads, 1 subscription

About: The package computes the optimal parameters for the Choquet kernel

Changes:

Initial Announcement on mloss.org.


Logo jackstraw 1.0

by nc - February 1, 2014, 22:53:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 838 views, 171 downloads, 1 subscription

About: Estimates statistical significance of association between variables and their principal components (PCs).

Changes:

Initial Announcement on mloss.org.


Logo Ordinal Choquistic Regression 1.00

by AliFall - January 30, 2014, 15:42:34 CET [ BibTeX BibTeX for corresponding Paper Download ] 755 views, 176 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo A Parallel LDA Learning Toolbox 1.0

by yanjianfeng - January 24, 2014, 11:48:07 CET [ BibTeX Download ] 617 views, 205 downloads, 1 subscription

About: We introduces PLL, a parallel LDA learning toolbox for big topic modeling.

Changes:

Initial Announcement on mloss.org.


Logo DRVQ 1.0.1-beta

by iavr - January 18, 2014, 17:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 780 views, 181 downloads, 1 subscription

About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast.

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 ] 9015 views, 1681 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 JMLR Darwin 1.7

by sgould - January 10, 2014, 01:33:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25125 views, 5351 downloads, 2 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.

Changes:

Version 1.7:

  • Log file now shows the command line
  • Utility application added for viewing multi-class segmentation legend
  • Added LBP filter response features to multi-class segmentation model
  • Added drwnColourHistogram class
  • Added k-means segmentation method for creating superpixels
  • Application visualizeSuperpixels and mex routines for loading and saving superpixels
  • Improved mex parsing of Matlab objects to support more matrix types
  • Bug fix in drwnOptimizer (thanks to Subarna Tripathi)
  • Updated copyright notice to 2007-2014
  • Other bug fixes and performance improvements

Version 1.6.1:

  • Maximum size of drwnShowDebuggingImage can be set from command line
  • Windows MSVC projects updated to link against OpenCV 2.4.6
  • Fixes for gcc 4.7 (thanks to Sarma Tangirala)
  • Bug fixes and performance improvements

Version 1.6:

  • Changed vision code from OpenCV 1.x C API to OpenCV 2.x C++ API
  • Added drwnHistogram class by Jason Corso
  • Added separate EPSG, EPSF and EPSX parameters to drwnOptimizer and changed signature of solve function
  • Added "-outUnary" option to inferPixelLabels for writing out unary potentials
  • Improved Matlab mex interfaces
  • Added drwnFeatureTransformFactory and improved drwnFactory class
  • Added drwnLinearTransform class
  • Bug fixes and performance improvements

Logo JMLR MLPACK 1.0.8

by rcurtin - January 7, 2014, 05:47:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29568 views, 5962 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 1 vote)

About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Memory leak in NeighborSearch index-mapping code fixed.
  • GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate().
  • Logistic regression implementation added in methods/logistic_regression.
  • Version information is now obtainable via mlpack::util::GetVersion() or the _MLPACKVERSION_MAJOR, _MLPACKVERSION_MINOR, and _MLPACKVERSION_PATCH macros.
  • Fix typos in allkfn and allkrann output.

Logo AIDE 0.2

by khalili - January 3, 2014, 18:01:06 CET [ Project Homepage BibTeX Download ] 738 views, 167 downloads, 1 subscription

About: AIDE (Automata Identification Engine) is a free open source tool for automata inference algorithms developed in C# .Net.

Changes:

Initial Announcement on mloss.org.


Showing Items 71-80 of 534 on page 8 of 54: First Previous 3 4 5 6 7 8 9 10 11 12 13 Next Last