Introduction to Statistical Relational Learning

Author: Lise Getoor,Ben Taskar

Publisher: MIT Press

ISBN: 0262072882

Category: Computers

Page: 586

View: 9713

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Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
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Large-Scale Machine Learning in the Earth Sciences

Author: Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser

Publisher: CRC Press

ISBN: 1315354462

Category: Computers

Page: 208

View: 7079

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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.
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Conformance Checking and Simulation-based Evolutionary Optimization for Deployment and Reconfiguration of Software in the Cloud

Author: Sören Frey

Publisher: BoD – Books on Demand

ISBN: 3732297349

Category: Computers

Page: 636

View: 6779

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Many SaaS providers nowadays want to leverage the cloud’s capabilities also for their existing applications, for example, to enable sound scalability and cost-effectiveness. This thesis provides the approach CloudMIG that supports SaaS providers to migrate those applications to IaaS and PaaS-based cloud environments. CloudMIG consists of a step-by-step process and focuses on two core components. (1) Restrictions imposed by specific cloud environments (so-called cloud environment constraints (CECs)), such as a limited file system access or forbidden method calls, can be validated by an automatic conformance checking approach. (2) A cloud deployment option (CDO) determines which cloud environment, cloud resource types, deployment architecture, and runtime reconfiguration rules for exploiting a cloud’s elasticity should be used. The implied performance and costs can differ in orders of magnitude. CDOs can be automatically optimized with the help of our simulation-based genetic algorithm CDOXplorer. Extensive lab experiments and an experiment in an industrial context show CloudMIG’s applicability and the excellent performance of its two core components.
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Communication, Management and Information Technology

International Conference on Communciation, Management and Information Technology (ICCMIT 2016, Cosenza, Italy, 26-29 April 2016)

Author: Marcelo Sampaio de Alencar

Publisher: CRC Press

ISBN: 149877945X

Category: Business & Economics

Page: 790

View: 7901

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Communication, Management and Information Technology contains the contributions presented at the International Conference on Communication, Management and Information Technology (ICCMIT 2016, Cosenza, Italy, 26-29 April 2016, organized by the Universal Society of Applied Research (USAR). The book aims at researchers, scientists, engineers, and scholar students interested or involved in Computer Science and Systems, Communication, and Management.
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Boosted Statistical Relational Learners

From Benchmarks to Data-Driven Medicine

Author: Sriraam Natarajan,Kristian Kersting,Tushar Khot,Jude Shavlik

Publisher: Springer

ISBN: 3319136445

Category: Computers

Page: 74

View: 5113

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This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.
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Artificial Intelligence

A Modern Approach

Author: Stuart J. Russell,Stuart Jonathan Russell,Peter Norvig,Ernest Davis

Publisher: Prentice Hall

ISBN: 0136042597

Category: Computers

Page: 1132

View: 4937

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Artificial intelligence: A Modern Approach, 3e,is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. It is also a valuable resource for computer professionals, linguists, and cognitive scientists interested in artificial intelligence. The revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
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Fifth International Workshop on Temporal Representation and Reasoning

Proceedings : Sanibel Island, Florida, May 16-17, 1998

Author: Lina Khatib,Robert Morris

Publisher: Institute of Electrical & Electronics Engineers(IEEE)

ISBN: 9780818684739

Category: Computers

Page: 191

View: 1747

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This volume addresses topics including: temporal reasoning in plan management; accounting for temporal evolutions in highly reactive decision-making; modelling problems; qualitative temporal reasoning; and quantitative structural temporal constraints on repeating events.
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Discovery Science

Second International Conference, DS'99, Tokyo, Japan, December 6-8, 1999 Proceedings

Author: Setsuo Arikawa,Koichi Furukawa

Publisher: Springer

ISBN: N.A

Category: Science

Page: 374

View: 6647

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This book constitutes the refereed proceedings of the Second International Conference on Discovery Science, DS'99, held in Tokyo, Japan, in December 1999. The 26 revised full papers presented together with 2 invited contributions and 25 poster presentations were carefully reviewed and selected from a total of 74 submissions. The following topics are covered in their relation to discovery science: logic, inference, algorithmic learning, heuristic search, database management, data mining, networking, inductive logic programming, abductive reasoning, machine learning, constructive programming, intelligent agents, statistical methods, visualization, HCI, etc.
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