Projects supporting the tab separated data format.


Logo ADAMS 0.4.6

by fracpete - June 23, 2014, 06:35:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7227 views, 1553 downloads, 1 subscription

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

Changes:
  • 15 new actors
  • new MEKA addons module (multi-label extension to WEKA)
  • overhauled plugin framework for ImageViewer and SpreadSheet file viewer
  • fixed twitter integration (replay of archives was broken)

Logo A Pattern Recognizer In Lua with ANNs v0.3.1

by pakozm - May 30, 2014, 10:49:10 CET [ Project Homepage BibTeX Download ] 2565 views, 605 downloads, 2 subscriptions

About: APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.

Changes:
  • Removed bugs.
  • Added Travis CI support.
  • KNN and clustering algorithms.
  • ZCA and PCA whitening.
  • Quickprop and ASGD optimization algorithms.
  • QLearning trainer.
  • Sparse float matrices are available in CSC an CSR formats.
  • Compilation with Homebrew and MacPorts available.
  • Compilation issues in Ubuntu 12.04 solved.

Logo MyMediaLite 3.10

by zenog - October 8, 2013, 22:29:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 42849 views, 8123 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 MLFlex 02-21-2012-00-12

by srp33 - April 3, 2012, 16:44:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1947 views, 391 downloads, 1 subscription

About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.)

Changes:

Initial Announcement on mloss.org.


Logo SFPD 1

by zenog - September 21, 2011, 14:26:45 CET [ Project Homepage BibTeX Download ] 1761 views, 424 downloads, 1 subscription

About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman.

Changes:

Initial Announcement on mloss.org.


Logo RRforest 2002-03-13

by zenog - September 21, 2011, 14:23:44 CET [ Project Homepage BibTeX Download ] 1742 views, 450 downloads, 1 subscription

About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman.

Changes:

Initial Announcement on mloss.org.


Logo Epistatic MAP Imputation 1.1

by colm - November 25, 2010, 21:01:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2715 views, 650 downloads, 1 subscription

About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression.

Changes:

Initial Announcement on mloss.org.