The Semantic Web. Latest Advances and New Domains, Harald Sack; Eva Blomqvist; Mathieu d`Aquin; Chiar
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642018815 ISBN-13(EAN): 9783642018817 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.
Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Автор: Fabien Gandon; Marta Sabou; Harald Sack; Claudia d Название: The Semantic Web. Latest Advances and New Domains ISBN: 3319188178 ISBN-13(EAN): 9783319188171 Издательство: Springer Рейтинг: Цена: 12298.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Vocabularies, Schemas, Ontologies.- Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment.- RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration.- VocBench: a Web Application for Collaborative Development of Multilingual Thesauri.- Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning.- Reasoning.- A Context-Based Semantics for SPARQL Property Paths over the Web.- Distributed and Scalable OWL EL Reasoning.- Large scale rule-based Reasoning using a Laptop.- RDF Digest: Efficient Summarization of RDF/S KBs.- Linked Data.- A Comparison of Data Structures to Manage URIs on the Web of Data.- Heuristics for Fixing Common Errors in Deployed schema.org Microdata.- Semantic Web and Web Science.- LOD-based Disambiguation of Named Entities in @tweets through Context enrichment.- Knowledge Enabled Approach to Predict the Location of Twitter Users.- Semantic Data Management, Big data, Scalability.- A Compact In-Memory Dictionary for RDF data.- Quality Assessment of Linked Datasets using Probabilistic Approximations.- Cooperative Techniques for SPARQL Query Relaxation in RDF Databases.- HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce.- Processing Aggregate Queries in a Federation of SPARQL Endpoints.- A survey of HTTP caching implementations on the open Semantic Web.- Query Execution Optimization for Clients of Triple Pattern Fragments.- Natural Language Processing and Information Retrieval LIME: the Metadata Module for OntoLex.- Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text.- HAWK Hybrid Question Answering using Linked Data.- Machine Learning.- Automating RDF Dataset Transformation and Enrichment.- Semi-supervised Instance Matching Using Boosted Classifiers.- Assigning Semantic Labels to Data Sources.- Inductive Classification through Evidence-based Models and their Ensembles.- Mobile Web, Internet of Things and Semantic Streams.- Standardized and Efficient RDF Encoding for Constrained Embedded Networks.- Services, Web APIs, and the Web of Things SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks.- Data as a Service: The Semantic Web Redeployed.- Cognition and Semantic Web.- On Coherent Indented Tree Visualization of RDF Graphs.- Gagg: A Graph Aggregation Operator.- FrameBase: Representing N-ary Relations using Semantic Frames.- Human Computation and Crowdsourcing.- Towards hybrid NER: a study of content and crowdsourcing-related performance factors.- Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets.- In-Use Industrial Track.- Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry.- Using Ontologies For Modeling Virtual Reality Scenarios.- Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes.- Crowdmapping Digital Social Innovation with Linked data.- Desperately searching for travel offers? Formulate better queries with some help from Linked Data.- Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing.- From Symptoms to Diseases - Creating the Missing Link.- Using semantic web technologies for enterprise architecture analysis.- PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain.
Описание: This book introduces a new paradigm called ‘Optimization in Changeable Spaces’ (OCS) as a useful tool for decision making and problem solving. It illustrates how OCS incorporates, searches, and constructively restructures the parameters, tangible and intangible, involved in the process of decision making. The book elaborates on OCS problems that can be modeled and solved effectively by using the concepts of competence set analysis, Habitual Domain (HD) and the mental operators called the 7-8-9 principles of deep knowledge of HD. In addition, new concepts of covering and discovering processes are proposed and formulated as mathematical tools to solve OCS problems. The book also includes reformulations of a number of illustrative real-life challenging problems that cannot be solved by traditional optimization techniques into OCS problems, and details how they can be addressed. Beyond that, it also includes perspectives related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery and knowledge extraction. This book will be of interest to managers of businesses and institutions, policy makers, and educators and students of decision making and behavior in DBA and/or MBA.
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642101860 ISBN-13(EAN): 9783642101861 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.
Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Автор: Alfio Gliozzo; Carlo Strapparava Название: Semantic Domains in Computational Linguistics ISBN: 3642425860 ISBN-13(EAN): 9783642425868 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An ideal text for researchers and graduate students, this comprehensive volume covers semantic domains and domain models, as well as ways of applying the technique to text categorization, word sense disambiguation, and cross-language text categorization.
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