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Showing Items 441-450 of 638 on page 45 of 64: First Previous 40 41 42 43 44 45 46 47 48 49 50 Next Last

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.

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Initial Announcement on mloss.org.


Logo GritBot 2.01

by zenog - September 2, 2011, 14:56:26 CET [ Project Homepage BibTeX Download ] 4054 views, 1032 downloads, 1 subscription

About: GritBot is an data cleaning and outlier/anomaly detection program.

Changes:

Initial Announcement on mloss.org.


Logo WebEnsemble 1.0

by jungc005 - May 8, 2012, 22:24:44 CET [ BibTeX Download ] 2600 views, 1023 downloads, 1 subscription

About: Use the power of crowdsourcing to create ensembles.

Changes:

Initial Announcement on mloss.org.


Logo PLEASD 0.1

by heroesneverdie - September 10, 2012, 03:53:26 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4542 views, 1021 downloads, 1 subscription

About: PLEASD: A Matlab Toolbox for Structured Learning

Changes:

Initial Announcement on mloss.org.


Logo Naive Bayes Classifier 1.0.0

by openpr_nlpr - December 2, 2011, 05:25:44 CET [ Project Homepage BibTeX Download ] 4509 views, 1018 downloads, 1 subscription

About: This program is a C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. The program uses the multinomial event model for representation, the maximum likelihood estimate with a Laplace smoothing technique for learning parameters. A sparse-data structure is defined to represent the feature vector in the program to seek higher computational speed.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-quantregForest 0.2-3

by r-cran-robot - June 1, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 4543 views, 1002 downloads, 0 subscriptions

About: Quantile Regression Forests

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Fetched by r-cran-robot on 2013-04-01 00:00:07.576421


Logo SparklingGraph 0.0.6

by riomus - June 17, 2016, 14:49:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4785 views, 990 downloads, 3 subscriptions

About: Large scale, distributed graph processing made easy.

Changes:

Bug fixes, Graph generators


Logo SketchSort 0.0.6

by ytabei - October 11, 2010, 18:33:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5347 views, 984 downloads, 1 subscription

About: A Sortware for All Pairs Similarity Search

Changes:

Initial Announcement on mloss.org.


Logo r-cran-penalizedSVM 1.1

by r-cran-robot - August 2, 2010, 00:00:00 CET [ Project Homepage BibTeX Download ] 4733 views, 983 downloads, 0 subscriptions

About: Feature Selection SVM using penalty functions

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Fetched by r-cran-robot on 2013-04-01 00:00:07.509844


Logo Naive Bayes EM Algorithm 1.0.0

by openpr_nlpr - December 2, 2011, 05:35:09 CET [ Project Homepage BibTeX Download ] 4663 views, 981 downloads, 1 subscription

About: OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. OpenPR-NBEM uses the multinomial event model for representation. The maximum likelihood estimate is used for supervised learning, and the expectation-maximization estimate is used for semi-supervised and un-supervised learning.

Changes:

Initial Announcement on mloss.org.


Showing Items 441-450 of 638 on page 45 of 64: First Previous 40 41 42 43 44 45 46 47 48 49 50 Next Last