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Grammatical Inference, de la Higuera



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јвтор: de la Higuera
Ќазвание:  Grammatical Inference
»здательство: Cambridge Academ
 лассификаци€:
»скусственный интеллект

ISBN: 0521763169
ISBN-13(EAN): 9780521763165
ISBN: 0-521-76316-9
ISBN-13(EAN): 978-0-521-76316-5
ќбложка/‘ормат: Hardback
—траницы: 432
¬ес: 0.9 кг.
ƒата издани€: 01.04.2010
—ери€: Pattern recognition and machine learning
язык: English
»ллюстрации: 170 b/w illus. 25 tables 1 music example 160 exercises
–азмер: 181 x 252 x 27
„итательска€ аудитори€: machine learning, computational linguistics
ќсновна€ тема: Computer science
ѕодзаголовок: Learning Automata and Grammars
—сылка на »здательство: Link
–ейтинг:
ѕоставл€етс€ из: јнглии
ќписание: The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.



Computer Age Statistical Inference

јвтор: Bradley Efron and Trevor Hastie
Ќазвание: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
»здательство: Cambridge Academ
–ейтинг:
÷ена: 5291 р.
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ќписание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Inference on the Low Level / An Investigation into Deduction, Nonmonotonic Reasoning, and the Philosophy of Cognition

јвтор: Leitgeb Hannes
Ќазвание: Inference on the Low Level / An Investigation into Deduction, Nonmonotonic Reasoning, and the Philosophy of Cognition
ISBN: 1402024924 ISBN-13(EAN): 9781402024924
»здательство: Springer
–ейтинг:
÷ена: 20376 р.
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ќписание: This monograph provides a new account of justified inference as a cognitive process. In contrast to the prevailing tradition in epistemology, the focus is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences. Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.

Argument, Inference and Dialectic / Collected Papers on Informal Logic

јвтор: Pinto R.C., Hansen Hans V.
Ќазвание: Argument, Inference and Dialectic / Collected Papers on Informal Logic
ISBN: 0792370058 ISBN-13(EAN): 9780792370055
»здательство: Springer
–ейтинг:
÷ена: 15674 р.
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ќписание: This volume contains 12 papers addressed to researchers and advanced students in informal logic and related fields, such as argumentation, formal logic, and communications. Among the issues are attempts to rethink the nature of argument and of inference, the role of dialectical context, and the standards for evaluating inferences, and to shed light on the interfaces between informal logic and argumentation theory, rhetoric, formal logic and cognitive psychology. The volume contains a concluding chapter that interrelates and qualifies the ideas developed in the individual papers.

Diagrammatic Representation and Inference / 4th International Conference, Diagrams 2006, Stanford, CA, USA, June 28-30, 2006, Proceedings

јвтор: Barker-Plummer Dave, Cox Richard, Swoboda Nik
Ќазвание: Diagrammatic Representation and Inference / 4th International Conference, Diagrams 2006, Stanford, CA, USA, June 28-30, 2006, Proceedings
ISBN: 3540356231 ISBN-13(EAN): 9783540356233
»здательство: Springer
–ейтинг:
÷ена: 7314 р.
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ќписание: This book constitutes the refereed proceedings of the 4th International Conference on Theory and Application of Diagrams, Diagrams 2006, held in Stanford, CA, USA in June 2006.The 13 revised full papers, 9 revised short papers, and 12 extended abstracts presented together with 2 keynote papers and 2 tutorial papers were carefully reviewed and selected from about 80 submissions. The papers are organized in topical sections on diagram comprehension by humans and machines, notations: history, design and formalization, diagrams and education, reasoning with diagrams by humans and machines, as well as psychological issues in comprehension, production and communication.

A Computational Model of Natural Language Communication / Interpretation, Inference, and Production in Database Semantics

јвтор: Hausser Roland
Ќазвание: A Computational Model of Natural Language Communication / Interpretation, Inference, and Production in Database Semantics
ISBN: 354035476X ISBN-13(EAN): 9783540354765
»здательство: Springer
–ейтинг:
÷ена: 7832 р.
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ќписание: Everyday life would be easier if we could simply talk with machines instead of having to program them. Before such talking robots can be built, however, there must be a theory of how communicating with natural language works. This requires not only a grammatical analysis of the language signs, but also a model of the cognitive agent, with interfaces for recognition and action, an internal database, and an algorithm for reading content in and out. In Database Semantics, these ingredients are used for reconstructing natural language communication as a mechanism for transferring content from the database of the speaker to the database of the hearer.Part I of this book presents a high-level description of an artificial agent which humans can freely communicate with in their accustomed language. Part II analyzes the major constructions of natural language, i.e., intra- and extrapropositional functor - argument structure, coordination, and coreference, in the speaker and the hearer mode. Part III defines declarative specifications for fragments of English, which are used for an implementation in Java. The ideal of using human language to control machines requires a practical theory of natural language communication that includes grammatical analysis of language signs, plus a model of the cognitive agent, with interfaces for recognition and action, an internal database, and an algorithm for reading content in and out. This book offers a functional framework for theoretical analysis of natural language communication and for practical applications of natural language processing.

Estimation of Dependences Based on Empirical Data / Empirical Inference Science

јвтор: Vapnik Vladimir, Kotz S.
Ќазвание: Estimation of Dependences Based on Empirical Data / Empirical Inference Science
ISBN: 0387308652 ISBN-13(EAN): 9780387308654
»здательство: Springer
–ейтинг:
÷ена: 19331 р.
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ќписание: In 1982, Springer published the English translation of the Russian book Estimation of Dependencies Based on Empirical Data which became the foundation of the statistical theory of learning and generalization (the VC theory). A number of new principles and new technologies of learning, including SVM technology, have been developed based on this theory.The second edition of this book contains two parts:- A reprint of the first edition which provides the classical foundation of Statistical Learning Theory- Four new chapters describing the latest ideas in the development of statistical inference methods. They form the second part of the book entitled Empirical Inference ScienceThe second part of the book discusses along with new models of inference the general philosophical principles of making inferences from observations. It includes new paradigms of inference that use non-inductive methods appropriate for a complex world, in contrast to inductive methods of inference developed in the classical philosophy of science for a simple world.The two parts of the book cover a wide spectrum of ideas related to the essence of intelligence: from the rigorous statistical foundation of learning models to broad philosophical imperatives for generalization.The book is intended for researchers who deal with a variety of problems in empirical inference: statisticians, mathematicians, physicists, computer scientists, and philosophers.

Information Algebras / Generic Structures for Inference

јвтор: Kohlas J√Љrg
Ќазвание: Information Algebras / Generic Structures for Inference
ISBN: 1852336897 ISBN-13(EAN): 9781852336899
»здательство: Springer
–ейтинг:
÷ена: 12539 р.
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ќписание: Information usually comes in pieces, from different sources. It refers to different, but related questions. Therefore information needs to be aggregated and focused onto the relevant questions. Considering combination and focusing of information as the relevant operations leads to a generic algebraic structure for information. This book introduces and studies information from this algebraic point of view. Algebras of information provide the necessary abstract framework for generic inference procedures. They allow the application of these procedures to a large variety of different formalisms for representing information. At the same time they permit a generic study of conditional independence, a property considered as fundamental for knowledge presentation. Information algebras provide a natural framework to define and study uncertain information. Uncertain information is represented by random variables that naturally form information algebras. This theory also relates to probabilistic assumption-based reasoning in information systems and is the basis for the belief functions in the Dempster-Shafer theory of evidence.

Diagrammatic Representation and Inference / Second International Conference, Diagrams 2002 Callaway Gardens, GA, USA, April 18-20, 2002 Proceedings

јвтор: Hegarty Mary, Meyer Bernd, Narayanan N. Hari
Ќазвание: Diagrammatic Representation and Inference / Second International Conference, Diagrams 2002 Callaway Gardens, GA, USA, April 18-20, 2002 Proceedings
ISBN: 3540435611 ISBN-13(EAN): 9783540435617
»здательство: Springer
–ейтинг:
÷ена: 8359 р.
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ќписание: This book constitutes the refereed proceedings of the Second International Conference Diagrams 2002, held in Callaway Gardens, Georgia, USA, in April 2002.The 21 revised full papers and 19 posters presented were carefully reviewed and selected from 77 submissions. The papers are organized in topical sections on understanding and communicating with diagrams, diagrams in mathematics, computational aspects of diagrammatic representation and reasoning, logic and diagrams, diagrams in human-computer interaction, tracing the process of diagrammatic reasoning, visualizing information with diagrams, diagrams and software engineering, and cognitive aspects.

Inference Control in Statistical Databases / From Theory to Practice

јвтор: Domingo-Ferrer Josep
Ќазвание: Inference Control in Statistical Databases / From Theory to Practice
ISBN: 3540436146 ISBN-13(EAN): 9783540436140
»здательство: Springer
–ейтинг:
÷ена: 7314 р.
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ќписание: Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source of data for producing statistics. Techniques used by intruders to make inferences compromising privacy increasingly draw on data mining, record linkage, knowledge discovery, and data analysis and thus statistical inference control becomes an integral part of computer science.This coherent state-of-the-art survey presents some of the most recent work in the field. The papers presented together with an introduction are organized in topical sections on tabular data protection, microdata protection, and software and user case studies.

Incomplete Information: Structure, Inference, Complexity

јвтор: Demri Stephane P., Orlowska Ewa S.
Ќазвание: Incomplete Information: Structure, Inference, Complexity
ISBN: 3540419047 ISBN-13(EAN): 9783540419044
»здательство: Springer
–ейтинг:
÷ена: 17241 р.
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ќписание: This monograph presents a systematic, exhaustive and up-to-date overview of formal methods and theories for data analysis and inference inspired by the concept of rough set. The book studies structures with incomplete information from the logical, algebraic and computational perspective. The formalisms developed are non-invasive in that only the actual information is needed in the process of analysis without external sources of information being required.The book is intended for researchers, lecturers and graduate students who wish to get acquainted with the rough set style approach to information systems with incomplete information.

A Logical Theory of Nonmonotonic Inference and Belief Change

јвтор: Bochman Alexander
Ќазвание: A Logical Theory of Nonmonotonic Inference and Belief Change
ISBN: 3540417664 ISBN-13(EAN): 9783540417668
»здательство: Springer
–ейтинг:
÷ена: 16196 р.
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ќписание: This monograph provides logical foundations and a uniform description for nonmonotonic reasoning and belief change. The approach to both these subjects is based on a powerful notion of an epistemic state that subsumes both existing models for nonmonotonic inference and current models for belief change. Many results and constructions in the book are completely new and have not appeared earlier in the literature.The book is primarily intended for experts in Artificial Intelligence and Knowledge Representation who are interested in tools for describing commonsense reasoning tasks as well as in representation capabilities of such tools. It is also of interest to general logicians.

Statistical and Inductive Inference by Minimum Message Length

јвтор: Wallace
Ќазвание: Statistical and Inductive Inference by Minimum Message Length
ISBN: 038723795X ISBN-13(EAN): 9780387237954
»здательство: Springer
–ейтинг:
÷ена: 16196 р.
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ќписание: The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the вАШbestвАЩ explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data.This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science.Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining."Any statistician interested in the foundations of the discipline, or the deeper philosophical issues of inference, will find this volume a rewarding read." Short Book Reviews of the International Statistical Institute,  December 2005


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