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Showing Items 161-170 of 550 on page 17 of 55: First Previous 12 13 14 15 16 17 18 19 20 21 22 Next Last

Logo r-cran-ahaz 1.14

by r-cran-robot - June 3, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 5162 views, 1101 downloads, 0 subscriptions

About: Regularization for semiparametric additive hazards regression

Changes:

Fetched by r-cran-robot on 2014-11-01 00:00:04.093763


Logo BCFWstruct 1.0.0

by ppletscher - June 25, 2013, 16:19:33 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1649 views, 384 downloads, 1 subscription

About: Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

Changes:

Initial Announcement on mloss.org.


Logo LibBi 1.0.0

by lawmurray - June 23, 2013, 09:04:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1559 views, 378 downloads, 1 subscription

About: Bayesian state-space modelling and inference on high-performance computer hardware.

Changes:

Initial Announcement on mloss.org.


About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level.

Changes:

Initial Announcement on mloss.org.


Logo ClusterEval 1.0

by cdevries - June 16, 2013, 04:15:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1643 views, 516 downloads, 1 subscription

About: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Changes:

Initial Announcement on mloss.org.


Logo AISAIC 1.0.0610

by fydennis - June 13, 2013, 21:54:55 CET [ BibTeX Download ] 1490 views, 870 downloads, 1 subscription

About: AISAIC software for analyzing human DNA copy numbers and detecting significant copy number alterations

Changes:

Initial Announcement on mloss.org.


About: This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classicalmean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.

Changes:

Initial Announcement on mloss.org.


About: Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Jstacs 2.1

by keili - June 3, 2013, 07:32:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14677 views, 3485 downloads, 2 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

New classes:

  • MultipleIterationsCondition: Requires another TerminationCondition to fail a contiguous, specified number of times
  • ClassifierFactory: Allows for creating standard classifiers
  • SeqLogoPlotter: Plot PNG sequence logos from within Jstacs
  • MultivariateGaussianEmission: Multivariate Gaussian emission density for a Hidden Markov Model
  • MEManager: Maximum entropy model

New features and improvements:

  • Alignment: Added free shift alignment
  • PerformanceMeasure and sub-classes: Extension to weighted test data
  • AbstractClassifier, ClassifierAssessment and sub-classes: Adaption to weighted PerformanceMeasures
  • DNAAlphabet: Parser speed-up
  • PFMComparator: Extension to PFM from other sources/databases
  • ToolBox: New convenience methods for computing several statistics (e.g., median, correlation)
  • SignificantMotifOccurrencesFinder: New methods for computing PWMs and statistics from predictions
  • SequenceScore and sub-classes: New method toString(NumberFormat)
  • DataSet: Adaption to weighted data, e.g., partitioning
  • REnvironment: Changed several methods from String to CharSequence

Restructuring:

  • changed MultiDimensionalSequenceWrapperDiffSM to MultiDimensionalSequenceWrapperDiffSS

Several minor new features, bug fixes, and code cleanups


Logo r-cran-CoxBoost 1.4

by r-cran-robot - November 1, 2014, 00:00:04 CET [ Project Homepage BibTeX Download ] 17568 views, 3543 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks

Changes:

Fetched by r-cran-robot on 2014-11-01 00:00:04.781816


Showing Items 161-170 of 550 on page 17 of 55: First Previous 12 13 14 15 16 17 18 19 20 21 22 Next Last