Introduction to Statistical Relational Learning

Author: Lise Getoor,Ben Taskar

Publisher: MIT Press

ISBN: 0262072882

Category: Computers

Page: 586

View: 5116


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.

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: 9536


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.

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: 3735715354

Category: Computers

Page: 636

View: 6962


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.

Probabilistic Graphical Models

Principles and Techniques

Author: Daphne Koller,Nir Friedman,Francis Bach

Publisher: MIT Press

ISBN: 0262013193

Category: Computers

Page: 1231

View: 4283


Proceedings of the annual Conference on Uncertainty in Artificial Intelligence, available for 1991-present. Since 1985, the Conference on Uncertainty in Artificial Intelligence (UAI) has been the primary international forum for exchanging results on the use of principled uncertain-reasoning methods in intelligent systems. The UAI Proceedings have become a basic reference for researches and practitioners who want to know about both theoretical advances and the latest applied developments in the field.

Artificial Intelligence

A Modern Approach

Author: Stuart Jonathan Russell,Peter Norvig

Publisher: Prentice Hall

ISBN: 0136042597

Category: Computers

Page: 1132

View: 5986


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.

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: 2867


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.

Discovery Science

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

Author: Setsuo Arikawa,Koichi Furukawa

Publisher: Springer


Category: Science

Page: 374

View: 1232


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.

2001 IEEE International Conference on Data Mining

Proceedings : 29 November-2 December, 2001, San Jose, California

Author: Nick Cercone,Tsau Y. Lin,Xindong Wu

Publisher: IEEE

ISBN: 9780769511191

Category: Computers

Page: 677

View: 4656


This proceedings of the November 2001 conference explores the design, analysis and implementation of data mining theory and systems. The 72 regular papers and 37 posters discuss data mining algorithms, data and knowledge representation, modeling of data to support data mining, scalability issues, st