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Showing Items 371-380 of 561 on page 38 of 57: First Previous 33 34 35 36 37 38 39 40 41 42 43 Next Last

Logo BCILAB 1.0-beta

by chkothe - January 6, 2012, 23:47:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3674 views, 726 downloads, 1 subscription

About: MATLAB toolbox for advanced Brain-Computer Interface (BCI) research.

Changes:

Initial Announcement on mloss.org.


Logo ROCCH 2.2

by tfawcett - January 9, 2010, 07:47:47 CET [ BibTeX BibTeX for corresponding Paper Download ] 2665 views, 725 downloads, 0 comments, 1 subscription

About: Given many points in ROC (Receiver Operator Characteristics) space, computes the convex hull.

Changes:

Initial Announcement on mloss.org.


About: Multi-class vector classification based on cost function-driven learning vector quantization , minimizing misclassification.

Changes:

Initial Announcement on mloss.org.


Logo Gibbs RTSS 1.0

by marc - April 4, 2011, 19:58:43 CET [ BibTeX BibTeX for corresponding Paper Download ] 2735 views, 719 downloads, 1 subscription

About: The software provides an implementation of a filter/smoother based on Gibbs sampling, which can be used for inference in dynamical systems.

Changes:

Initial Announcement on mloss.org.


Logo QuickDT 0.1

by sanity - September 21, 2011, 13:43:37 CET [ Project Homepage BibTeX Download ] 2426 views, 718 downloads, 1 subscription

About: A decision tree learner that is designed to be reasonably fast, but the primary goal is ease of use

Changes:

Initial Announcement on mloss.org.


Logo libcmaes 0.9.4

by beniz - January 8, 2015, 11:09:02 CET [ Project Homepage BibTeX Download ] 3436 views, 713 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

Changes:

Update works around clang bug (e.g. for OSX) and implements uncertainty handling scheme. Main changes:

  • work around clang bug, now working with clang, ref #19

  • easier build on OSX

  • added uncertainty handling scheme for noisy objective functions, ref #65

  • optional support for surrogates at compile time, reducing the overal lib size, ref #90

  • fixed uninstall of python bindings


About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are under-determined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping.

Changes:

Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs

Version 1.0 (Nov 9, 2011) * 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 ] 2939 views, 695 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.


Logo C5.0 2.07

by zenog - September 2, 2011, 14:49:04 CET [ Project Homepage BibTeX Download ] 2727 views, 687 downloads, 1 subscription

About: C5.0 is the successor of the C4.5 decision tree algorithm/tool. In particular, it is faster and more memory-efficient.

Changes:

Initial Announcement on mloss.org.


Logo Action Recognition by Dense Trajectories 1.0

by openpr_nlpr - June 6, 2012, 11:38:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3582 views, 679 downloads, 1 subscription

About: The code is for computing state-of-the-art video descriptors for action recognition. The most up-to-date information can be found at: http://lear.inrialpes.fr/people/wang/dense_trajectories

Changes:

Initial Announcement on mloss.org.


Showing Items 371-380 of 561 on page 38 of 57: First Previous 33 34 35 36 37 38 39 40 41 42 43 Next Last