Incorporating Evidence in Bayesian Networks with the Select Operator - all 4 versions »
CJ Butz, F Fang - Advances in Artificial Intelligence: 18th Conference of the …, 2005 - books.google.com
"Here's a preliminary data mining analysis of musical social networking service Last.fm. An automated classification into clusters or sub populations with related musical genres reveals the structure of musical preferences among the users in a relatively large sample population. Musical tag clouds are adopted to characterise users and populations, which adds a highly descriptive value and aids with the interpretation of the results."
Our in intention is to construct a repository that will allow us empirical research within our community by facilitating (1)better reproducibility of results, and (2) better comparisons among competing approach. Both of these are required to measure progress on problems that are commonly agreed upon, such as inference and learning
Prem Melville and Raymond J. Mooney and Ramadass Nagarajan. Content-Boosted Collaborative Filtering for Improved Recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence(AAAI-2002),
pp. 187-192, Edmonton, Canada, July 2002
Bayesian Networks are probabilistic structured representations of domains which have been applied to monitoring and manipulating cause and effects for modelled systems as disparate as the weather, disease and mobile telecommunications networks. Although useful, Bayesian Networks are notoriously difficult to build accurately and efficiently which has somewhat limited their application to real world problems. Ontologies are also a structured representation of knowledge, encoding facts and rules about a given domain. This paper outlines an approach to harness the knowledge and inference capabilities inherent in an ontology model to automate the construction of Bayesian Networks to accurately represent a domain of interest. The approach was implemented in the context of an adaptive, self-configuring network management system in the telecommunications domain. In this system, the ontology model has the dual function of knowledge repository and facilitator of automated workflows and the generated BN serves to monitor effects of management activity, forming part of a feedback look for self-configuration decisions and tasks.
"In Semantic Web languages, such as RDF and OWL, a property is a binary relation: it is used to link two individuals or an individual and a value. However, in some cases, the natural and convenient way to represent certain concepts is to use relations to link an individual to more than just one individual or value. These relations are called n-ary relations. For example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. Another example is representing relations among multiple individuals, such as a buyer, a seller, and an object that was bought when describing a purchase of a book. This document presents ontology patterns for representing n-ary relations in RDF and OWL and discusses what users must consider when choosing these patterns."
Proceedings of KDD Cup and Workshop 2007
The Workshop was Co-organized by ACM SIGKDD and Netflix
Held during KDD-2007, San Jose, California, Aug 12, 2007
Welcome to the OpenMath website. OpenMath is an extensible standard for representing the semantics of mathematical objects. If you haven't heard about it before you might want to consult the overview.
The OWL API is a Java API and reference implmentation for creating, manipulating and serialising OWL Ontologies. The latest version of the API is focused towards OWL 2
Fang Wu and Bernardo A. Huberman
HP Laboratories
Palo Alto, CA 94304
January 23, 2008
Abstract
We analyze the role that popularity and novelty play in attracting
the attention of users to dynamic websites. We do so by determining
the performance of three different strategies that can be utilized to
maximize attention. The first one prioritizes novelty while the second
emphasizes popularity. A third strategy looks myopically into
the future and prioritizes stories that are expected to generate the
most clicks within the next few minutes. We show that the first two
strategies should be selected on the basis of the rate of novelty decay,
while the third strategy performs sub-optimally in most cases. We also
demonstrate that the relative performance of the first two strategies
as a function of the rate of novelty decay changes abruptly around a
critical value, resembling a phase transition in the physical world.
Die gezeigten Posts sind eventuell nicht akkurat bei Änderungen, die vor Kurzem vorgenommen worden. Wollen Sie jedoch akkurate Posts mit eingeschränkten Sortierungsmöglichkeiten, folgen Sie dem folgenden Link.
M. McLaughlin, und J. Herlocker. SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, Seite 329--336. New York, NY, USA, ACM Press, (2004)
B. Kim, Q. Li, und A. Howe. WWW '06: Proceedings of the 15th international conference on World Wide Web, Seite 973--974. New York, NY, USA, ACM Press, (2006)