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Showing Items 141-150 of 595 on page 15 of 60: First Previous 10 11 12 13 14 15 16 17 18 19 20 Next Last

Logo 1SpectralClustering 1.1

by tbuehler - June 27, 2011, 10:45:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8543 views, 1669 downloads, 1 subscription

About: A fast and scalable graph-based clustering algorithm based on the eigenvectors of the nonlinear 1-Laplacian.

Changes:
  • fixed bug occuring when input graph is disconnected
  • reduced memory usage when input graph has large number of disconnected components
  • more user-friendly usage of main method OneSpectralClustering
  • faster computation of eigenvector initialization + now thresholded according to multicut-criterion
  • several internal optimizations

Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8525 views, 3050 downloads, 2 subscriptions

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Changes:

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

  1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
  2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
  3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
  4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.


Logo PredictionIO 0.7.0

by simonc - April 29, 2014, 20:59:57 CET [ Project Homepage BibTeX Download ] 8521 views, 1768 downloads, 2 subscriptions

About: Open Source Machine Learning Server

Changes:
  • Single machine version for small-to-medium scale deployments
  • Integrated GraphChi (disk-based large-scale graph computation) and algorithms: ALS, CCD++, SGD, CLiMF
  • Improved runtime for training and offline evaluation
  • Bug fixes

See release notes - https://predictionio.atlassian.net/secure/ReleaseNote.jspa?projectId=10000&version=11801


Logo Pattern 2.4

by tomdesmedt - August 31, 2012, 02:26:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8512 views, 2416 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 XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 8511 views, 1675 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

Changes:
  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version


About: Matlab code for performing variational inference in the Indian Buffet Process with a linear-Gaussian likelihood model.

Changes:

Initial Announcement on mloss.org.


Logo Dependency modeling toolbox 0.2

by lml - April 30, 2010, 14:38:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8339 views, 1294 downloads, 1 subscription

About: Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The demand for such methods is increasing with the availability and size of co-occurring observations in computational biology, open data initiatives, and in other domains. We provide practical, open access implementations of general-purpose algorithms that help to realize the full potential of these information sources.

Changes:

Three independent modules (drCCA, pint, MultiWayCCA) have been added.


About: The High-Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifier for high-dimensional data. This classifier is based on a mixture of Gaussian models adapted for [...]

Changes:

Initial Announcement on mloss.org.


Logo Adaptive Resonance Theory for Unsupervised Learning 1.0

by ibaze - November 13, 2008, 16:40:28 CET [ Project Homepage BibTeX Download ] 8308 views, 1974 downloads, 1 subscription

About: This software package includes the ART algorithms for unsupervised learning only. It is a family of four programs based on different ART algorithms (ART 1, ART 2A, ART 2A-C and ART Distance). All of [...]

Changes:

Initial Announcement on mloss.org.


Logo BSVM 2.06

by biconnect - January 30, 2008, 10:27:13 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8241 views, 1580 downloads, 1 subscription

About: BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods

Changes:

Initial Announcement on mloss.org.


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