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Logo Graphical Models and Conditional Random Fields Toolbox 2

by jdomke - January 5, 2012, 15:38:20 CET [ Project Homepage BibTeX Download ] 2466 views, 603 downloads, 1 subscription

About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the high-treewidth setting.

Changes:

Initial Announcement on mloss.org.


Logo GritBot 2.01

by zenog - September 2, 2011, 14:56:26 CET [ Project Homepage BibTeX Download ] 2423 views, 601 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo OpenGM 2 2.0.2 beta

by opengm - June 1, 2012, 14:33:53 CET [ Project Homepage BibTeX Download ] 2591 views, 600 downloads, 1 subscription

About: A C++ Library for Discrete Graphical Models

Changes:

Initial Announcement on mloss.org.


Logo treelearn 1

by iskander - September 21, 2011, 16:12:27 CET [ Project Homepage BibTeX Download ] 2455 views, 595 downloads, 1 subscription

About: A python implementation of Breiman's Random Forests.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-rda 1.0.2-2

by r-cran-robot - June 30, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 2721 views, 594 downloads, 0 subscriptions

About: Shrunken Centroids Regularized Discriminant Analysis

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:07.868841


Logo r-cran-penalizedSVM 1.1

by r-cran-robot - August 2, 2010, 00:00:00 CET [ Project Homepage BibTeX Download ] 2797 views, 594 downloads, 0 subscriptions

About: Feature Selection SVM using penalty functions

Changes:

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


Logo Naive Bayes Classifier 1.0.0

by openpr_nlpr - December 2, 2011, 05:25:44 CET [ Project Homepage BibTeX Download ] 2268 views, 592 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 Local Binary Pattern 1.0.0

by openpr_nlpr - December 2, 2011, 05:33:44 CET [ Project Homepage BibTeX Download ] 1689 views, 589 downloads, 1 subscription

About: This is a class to calculate histogram of LBP (local binary patterns) from an input image, histograms of LBP-TOP (local binary patterns on three orthogonal planes) from an image sequence, histogram of the rotation invariant VLBP (volume local binary patterns) or uniform rotation invariant VLBP from an image sequence.

Changes:

Initial Announcement on mloss.org.


Logo PyStruct 0.2

by t3kcit - July 9, 2014, 09:29:23 CET [ Project Homepage BibTeX Download ] 2124 views, 584 downloads, 1 subscription

About: PyStruct is a framework for learning structured prediction in Python. It has a modular interface, similar to the well-known SVMstruct. Apart from learning algorithms it also contains model formulations for popular CRFs and interfaces to many inference algorithm implementation.

Changes:

Initial Announcement on mloss.org.


About: In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2-norm or L1-norm, the MaxEnt-PCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEnt-PCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEnt-PCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods.

Changes:

Initial Announcement on mloss.org.


Showing Items 401-410 of 561 on page 41 of 57: First Previous 36 37 38 39 40 41 42 43 44 45 46 Next Last