Îïèñàíèå: This book constitutes the refereed proceedings of the 7th International Colloquium on Grammatical Inference, ICGI 2004, held in Athens, Greece in October 2004.The 20 revised full papers and 8 revised poster papers presented together with 3 invited contributions were carefully reviewed and selected from 45 submissions. The topics of the papers presented range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to estimations of probabilistic grammars.

Îïèñàíèå: Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This 1996 book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modelling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between different perspectives both within chapters, which draw on insights from experimental and computational work, and between chapters, through commentaries written by the contributors on each others' work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection.

Îïèñàíèå: 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.

Àâòîð: Kohlas JÃ¼rg Íàçâàíèå: Information Algebras / Generic Structures for Inference ISBN: 1852336897 ISBN-13(EAN): 9781852336899 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 17819 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Îïèñàíèå: 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.

Îïèñàíèå: 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.

Îïèñàíèå: 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.

This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed.

Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others.

Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>

Îïèñàíèå: A book that constitutes the refereed proceedings of the 10th International Colloquium on Grammatical Inference, ICGI 2010, that was held in Valencia, Spain, in September 2010.

Îïèñàíèå: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical Evolution's rich modularity gives a unique flexibility, making it possible to use alternative search strategies - whether evolutionary, deterministic or some other approach - and to even radically change its behavior by merely changing the grammar supplied. This approach to Genetic Programming represents a powerful new weapon in the Machine Learning toolkit that can be applied to a diverse set of problem domains. Beginning with an overview of the necessary background material in Genetic Programming and Molecular Biology, Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language outlines the current state of the art in grammatical and genotype-phenotype-based approaches. Following a description of Grammatical Evolution and its application to a number of example problems, an in-depth analysis of the approach is conducted, focusing on areas such as the degenerate genetic code, wrapping, and crossover. The book continues with a description of hot topics in Grammatical Evolution and presents possible directions for future research.

Îïèñàíèå: 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.

Îïèñàíèå: 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 â€˜bestâ€™ 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