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This video was recorded at REASE. This is a one-hour video recording of the presentation of John Domingue at the First Asian Autumn School on the Semantic Web. It comprises two videos synchronized with the slides (requires Flash) or the videos alone. Table of Contents: Semantic Web Services: Application Areas Contents DIP DIP Consortium Supporting Emergency Planning for Essex County Council Essex County Council Emergency Planning Context Emergency planning scenario eMerges Ontologies Generic Application Structure Demonstration of Emergency Planning (GIS) Prototype V1 EMerges Prototype Architecture SWS and Business Process Modelling Super Project SUPER Consortium Motivation Querying the Process Space The Critical IT / Process Divide What Are My Services? What are my services? Matching Activities and Port Types Based on Semantics Supporting Business Users Better Matching Model Representations & Semantics The SUPER Stack Modelling Stack Telecommunications Solution Map Content on Demand Digital Rights Management & Content Procurement Modelling Stack Business Process Notations Modelling Stack Programming Model Deploying Applications Business Protocols Modelling Stack WSMO Top Level Notions The SUPER Trinity SUPER Methodology SUPER Architecture SUPER Ontology Stack Super Demo Context Prototype Scenario Digital Asset Management BPMN Service/Process Catalogue Super Demo Video
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- Computing
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- Semantic integration (Computer systems) Semantic Web
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This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Hierarchical modeling and reasoning are fundamental in machine intelligence, and for this the two-parameter Poisson-Dirichlet Process (PDP) plays an important role. The most popular MCMC sampling algorithm for the hierarchical PDP and hierarchical Dirichlet Process is to conduct an incremental sampling based on the Chinese restaurant metaphor, which originates from the Chinese restaurant process (CRP). In this paper, with the same metaphor, we propose a new table representation for the hierarchical PDPs by introducing an auxiliary latent variable, called table indicator, to record which customer takes responsibility for starting a new table. In this way, the new representation allows full exchangeability that is an essential condition for a correct Gibbs sampling algorithm. Based on this representation, we develop a block Gibbs sampling algorithm, which can jointly sample the data item and its table contribution. We test this out on the hierarchical Dirichlet process variant of latent Dirichlet allocation (HDP-LDA) developed by Teh, Jordan, Beal and Blei. Experiment results show that the proposed algorithm outperforms their "posterior sampling by direct assignment" algorithm in both out-of-sample perplexity and convergence speed. The representation can be used with many other hierarchical PDP models.
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence
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