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URLearning - IndexURLearning (\
http://www.urlearning.org/
URLearning (\
http://www.urlearning.org/
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URLearning - Index | urlearning.org Reviews
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URLearning (\
urlearning.org
URLearning - Aboutus
http://www.urlearning.org/aboutus.html
A Software for Learning Bayesian Networks. Changhe Yuan, Ph.D. Brandon Malone, Ph.D. 2015 Uncertainty Reasoning Laboratory.
URLearning - Download
http://www.urlearning.org/download.html
A Software for Learning Bayesian Networks. How to use URLearing? This is software we have developed for learning a Bayesian network structure for a dataset which optimizes the score for a dataset. The C source code. Is available as a netbeans project. It requires Boost (C source libraries). This file describes the necessary steps to configure netbeans to compile the code. This file describes the necessary steps to add a new configuration (algorithm) to the project. There is also an older Java version.
URLearning - Publications
http://www.urlearning.org/publications.html
A Software for Learning Bayesian Networks. Xiannian Fan, Changhe Yuan. An Improved Lower Bound for Bayesian Network Structure Learning. In Proceedings of the 29th AAAI Conference (AAAI-15). Austin, Texas. 2015. Xiannian Fan, Brandon Malone, Changhe Yuan. Finding Optimal Bayesian Networks Using Constraints Learned from Data. In Proceedings of the 30th Annual Conference on Uncertainty in Artificial Intelligence (UAI-14). Quebec City, Quebec. 2014. Xiannian Fan, Changhe Yuan, Brandon Malone. Learning optima...
URLearning - Index
http://www.urlearning.org/index.html
A Software for Learning Bayesian Networks. Ldquo;You Are Learning! Rdquo;) is an open source C/C software for learning Bayesian network structures from complete discrete data. In the software, the learning problem is formulated as a shortest path problem; various heuristic search algorithms such as A* and breadth-first branch and bound have been applied to solve the problem based on novel bounds and heuristic functions. 2015 Uncertainty Reasoning Laboratory.
URLearning - Datasets
http://www.urlearning.org/datasets.html
A Software for Learning Bayesian Networks. AAAI 2014; UAI 2014. Prior to UAI 2013. These packages contain all of the files necessary to reproduce the experiments in many of our papers. UAI 2015 - Quality Study. This was a large study investigating the quality of Bayesian network structures learned using a variety of exact and approximate algorithms. AAAI 2014 - Portfolio Study. AAAI 2014; UAI 2014. Published datasets (csv, pss, binary scores). Gives runtime information on using the. The input files neces...
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Uncertainty Reasoning Laboratory
http://url.cs.qc.cuny.edu/software.html
URLearning ("You are learning") is a software package that implements several search algorithms for learning optimal Bayesian networks. Both C source code and executable files are available for download. MRE (Most Relevant Explanation) is a new inference task in Bayesian networks that finds the most relevant partial instantiation of target variables as the explanation for given evidence. Department of Computer Science. Flushing, NY 11367, Office: (718) 997-3484.
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URLearning - Index
A Software for Learning Bayesian Networks. Ldquo;You Are Learning! Rdquo;) is an open source C/C software for learning Bayesian network structures from complete discrete data. In the software, the learning problem is formulated as a shortest path problem; various heuristic search algorithms such as A* and breadth-first branch and bound have been applied to solve the problem based on novel bounds and heuristic functions. 2015 Uncertainty Reasoning Laboratory.
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UrlEater - Big Things, Small Packages
How do I get started? How do I add a tag? How do I delete tag? Can I get a complete list of commands? Why would I use UrlEater? How do I add an EatIt! Button to Internet Explorer? Including the scheme, e.g. http:/ , ftp:/ , etc. Complete the following fields to create an account and begin managing your own custom tags. If you plan to share this account, you can choose to require a key in order to make changes to the tag list. See the Learn More page for more info. Tag] /add [description] [url].
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