Predicting Structured Data

Predicting Structured Data PDF Author: Neural Information Processing Systems Foundation
Publisher: MIT Press
ISBN: 0262026171
Category : Algorithms
Languages : en
Pages : 361

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Book Description
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

Predicting Structured Data

Predicting Structured Data PDF Author: Neural Information Processing Systems Foundation
Publisher: MIT Press
ISBN: 0262026171
Category : Algorithms
Languages : en
Pages : 361

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Book Description
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

Product Life-Cycle Management

Product Life-Cycle Management PDF Author: Max Giordano
Publisher: John Wiley & Sons
ISBN: 1118587529
Category : Business & Economics
Languages : en
Pages : 389

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Book Description
This book gives a comprehensive view of the most recent major international research in the field of tolerancing, and is an excellent resource for anyone interested in Computer Aided Tolerating. It is organized into 4 parts. Part 1 focuses on the more general problems of tolerance analysis and synthesis, for tolerancing in mechanical design and manufacturing processes. Part 2 specifically highlights the simulation of assembly with defects, and the influence of tolerances on the quality of the assembly. Part 3 deals with measurement aspects, and quality control throughout the life cycle. Different measurement technologies and methods for estimating uncertainty are considered. In Part 4, different aspects of tolerancing and their interactions are explored, from the definition of functional requirement to measurement processes in a PLM approach.

An Introduction to Computational Learning Theory

An Introduction to Computational Learning Theory PDF Author: Michael J. Kearns
Publisher: MIT Press
ISBN: 9780262111935
Category : Computers
Languages : en
Pages : 230

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Book Description
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory PDF Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324

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Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Reinforcement Learning, second edition

Reinforcement Learning, second edition PDF Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549

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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Generalized Structured Component Analysis

Generalized Structured Component Analysis PDF Author: Heungsun Hwang
Publisher: CRC Press
ISBN: 146659294X
Category : Mathematics
Languages : en
Pages : 346

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Book Description
Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new approach and apply it to their own research. The book emphasizes conceptual discussions throughout while relegating more technical intricacies to the chapter appendices. Most chapters compare generalized structured component analysis to partial least squares path modeling to show how the two component-based approaches differ when addressing an identical issue. The authors also offer a free, online software program (GeSCA) and an Excel-based software program (XLSTAT) for implementing the basic features of generalized structured component analysis.

Digital Transformation in Financial Services

Digital Transformation in Financial Services PDF Author: Claudio Scardovi
Publisher: Springer
ISBN: 3319669451
Category : Business & Economics
Languages : en
Pages : 242

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Book Description
This book analyzes the set of forces driving the global financial system toward a period of radical transformation and explores the transformational challenges that lie ahead for global and regional or local banks and other financial intermediaries. It is explained how these challenges derive from the newly emerging post-crisis structure of the market and from shadow and digital players across all banking operations. Detailed attention is focused on the impacts of digitalization on the main functions of the financial system, and particularly the banking sector. The author elaborates how an alternative model of banking will enable banks to predict, understand, navigate, and change the external ecosystem in which they compete. The five critical components of this model are data and information mastering; effective use of applied analytics; interconnectivity and “junction playing”; development of new business solutions; and trust and credibility assurance. The analysis is supported by a number of informative case studies. The book will be of interest especially to top and middle managers and employees of banks and financial institutions but also to FinTech players and their advisers and others.

Graphics Recognition. Algorithms and Applications

Graphics Recognition. Algorithms and Applications PDF Author: Dorothea Blostein
Publisher: Springer
ISBN: 9783662172940
Category : Computers
Languages : en
Pages : 370

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Book Description
This book presents refereed and revised papers presented at GREC 2001, the 4th IAPR International Workshop on Graphics Recognition, which took place in Kingston, Ontario, Canada in September 2001. Graphics recognition is a branch of document image analysis that focuses on the recognition of two-dimensional notations such as engineering drawings, maps, mathematical notation, music notation, tables, and chemical structure diagrams. Due to the growing demand for both o?-line and on-line document recognition systems, the ?eld of graphics recognition has an excitingand promisingfuture. The GREC workshops provide an opportunity for researchers at all levels of experience to share insights into graphics recognition methods. The workshops enjoy strongparticipation from researchers in both industry and academia. They are sponsored by IAPR TC-10, the Technical Committee on Graphics Recog- tion within the International Association for Pattern Recognition. Edited v- umes from the previous three workshops in this series are available as Lecture Notes in Computer Science, Vols. 1072, 1389, and 1941. After the GREC 2001 workshop, authors were invited to submit enhanced versions of their papers for review. Every paper was evaluated by three reviewers. We are grateful to both authors and reviewers for their careful work during this review process. Many of the papers that appear in this volume were thoroughly revised and improved, in response to reviewers’ suggestions.

Graphics Recognition

Graphics Recognition PDF Author: Young-Bin Kwon
Publisher: Springer
ISBN: 9783642368233
Category : Computers
Languages : en
Pages : 0

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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Workshop on Graphics Recognition (GREC 2011), held in Seoul, Korea, September 15-16, 2011. The 25 revised full papers presented were carefully selected from numerous submissions. Graphics recognition is a subfield of document image analysis that deals with graphical entities in engineering drawings, sketches, maps, architectural plans, musical scores, mathematical notation, tables, and diagrams. Accordingly the conference papers are organized in 5 technical sessions, covering the topics such as map and ancient documents, symbol and logo recognition, sketch and drawings, performance evaluation and challenge processing.

Simply This... Amen.

Simply This... Amen. PDF Author: Donna McLin
Publisher:
ISBN: 9781701917064
Category :
Languages : en
Pages : 382

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Book Description
This prayer book was written over a two-year span. The prayers started one night when my daughter and I could not sync our time to pray together on one particular night in March 2017. While preparing for bed, the Lord said to me, "Just write a prayer and put it on Facebook". I did and the rest is history. You are now holding what it is that the Lord has done with these prayers. Each night with the exception of being away from home with no phone service, or during times of fasting, I wrote a prayer each night and posted them on Facebook. I have been told by many people along the way that they do not know how to pray. It is my prayer that this book will be a foundation for those who feel that they do not know how to pray or who simply need a prayer devotional.