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Patterns And Inferential Networks
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Book Synopsis Inferential Network Analysis by : Skyler J. Cranmer
Download or read book Inferential Network Analysis written by Skyler J. Cranmer and published by Cambridge University Press. This book was released on 2020-11-19 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.
Book Synopsis Pattern-Directed Inference Systems by : D. A. Waterman
Download or read book Pattern-Directed Inference Systems written by D. A. Waterman and published by Academic Press. This book was released on 2014-05-10 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern-Directed Inference Systems provides a description of the design and implementation of pattern-directed inference systems (PDIS) for various applications. The book also addresses the theoretical significance of PDIS for artificial intelligence and cognitive psychology. The book is divided into eight sections. The introduction provides a brief overview of pattern-directed inference systems, including a historical perspective, a review of basic concepts, and a survey of work in this area. Subsequent chapters address topics on architecture and design, methods for accessing and controlling rule based systems, methods for obtaining adaptive behavior via rule-based systems and cognitive modeling. Constructing models of human information processing, natural language understanding and multilevel systems and complexity are described as well. The last section discusses the earlier chapters in the book and provides a unifying set of principles for the PDIS formalism. Computer scientists, psychologists, engineers, and researchers in artificial intelligence will find the book very informative.
Book Synopsis Inferential Network Analysis by : Skyler J. Cranmer
Download or read book Inferential Network Analysis written by Skyler J. Cranmer and published by Cambridge University Press. This book was released on 2020-11-19 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.
Book Synopsis Neural Networks for Knowledge Representation and Inference by : Daniel S. Levine
Download or read book Neural Networks for Knowledge Representation and Inference written by Daniel S. Levine and published by Psychology Press. This book was released on 2013-04-15 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.
Book Synopsis Grammatical Inference: Learning Syntax from Sentences by : Laurent Miclet
Download or read book Grammatical Inference: Learning Syntax from Sentences written by Laurent Miclet and published by Springer Science & Business Media. This book was released on 1996-09-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Colloquium on Grammatical Inference, ICGI-96, held in Montpellier, France, in September 1996. The 25 revised full papers contained in the book together with two invited key papers by Magerman and Knuutila were carefully selected for presentation at the conference. The papers are organized in sections on algebraic methods and algorithms, natural language and pattern recognition, inference and stochastic models, incremental methods and inductive logic programming, and operational issues.
Book Synopsis Prediction and Inference from Social Networks and Social Media by : Jalal Kawash
Download or read book Prediction and Inference from Social Networks and Social Media written by Jalal Kawash and published by Springer. This book was released on 2017-03-16 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.
Book Synopsis Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications by : Isabelle Bloch
Download or read book Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications written by Isabelle Bloch and published by Springer. This book was released on 2010-11-02 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is a central topic in contemporary computer sciences, with continuously evolving topics, challenges, and methods, including machine learning, content-based image retrieval, and model- and knowledge-based - proaches, just to name a few. The Iberoamerican Congress on Pattern Recog- tion (CIARP) has become established as a high-quality conference, highlighting the recent evolution of the domain. These proceedings include all papers presented during the 15th edition of this conference, held in Sao Paulo, Brazil, in November 2010. As was the case for previous conferences, CIARP 2010 attracted parti- pants from around the world with the aim of promoting and disseminating - going research on mathematical methods and computing techniques for pattern recognition, computer vision, image analysis, and speech recognition, as well as their applications in such diverse areas as robotics, health, entertainment, space exploration, telecommunications, data mining, document analysis, and natural language processing and recognition, to name only a few of them. Moreover, it provided a forum for scienti?c research, experience exchange, sharing new kno- edge and increasing cooperation between research groups in pattern recognition and related areas. It is important to underline that these conferences have contributed sign- icantly to the growth of national associations for pattern recognition in the Iberoamerican region, all of them as members of the International Association for Pattern Recognition (IAPR).
Book Synopsis Granular Neural Networks, Pattern Recognition and Bioinformatics by : Sankar K. Pal
Download or read book Granular Neural Networks, Pattern Recognition and Bioinformatics written by Sankar K. Pal and published by Springer. This book was released on 2017-05-02 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing.
Book Synopsis Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science by : Franco Taroni
Download or read book Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science written by Franco Taroni and published by John Wiley & Sons. This book was released on 2014-07-21 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks “This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.” Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates diffculties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.
Book Synopsis Grammatical Inference: Algorithms and Applications by : Arlindo L. Oliveira
Download or read book Grammatical Inference: Algorithms and Applications written by Arlindo L. Oliveira and published by Springer. This book was released on 2004-02-13 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Colloquium on Grammatical Inference, ICGI 2000, held in Lisbon, Portugal in September 2000. The 24 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers address topics like machine learning, automata, theoretical computer science, computational linguistics, pattern recognition, artificial neural networks, natural language acquisition, computational biology, information retrieval, text processing, and adaptive intelligent agents.
Book Synopsis Flexible Query Answering Systems by : Troels Andreasen
Download or read book Flexible Query Answering Systems written by Troels Andreasen and published by Springer Science & Business Media. This book was released on 2009-10-15 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Flexible Query Answering Systems, FQAS 2009, held in Roskilde, Denmark, in October 2009. The 57 papers included in this volume were carefully reviewed and selected from 90 submissions. They are structured in topical sections on database management, information retrieval, extraction and mining, ontologies and semantic web, intelligent information extraction from texts, advances in fuzzy querying, personalization, preferences, context and recommendation, and Web as a stream.
Book Synopsis Pattern Recognition by : Zeynep Akata
Download or read book Pattern Recognition written by Zeynep Akata and published by Springer Nature. This book was released on 2021-03-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tübingen, Germany, but had to change to an online format due to the COVID-19 pandemic. The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning.
Book Synopsis Statistical Inference from High Dimensional Data by : Carlos Fernandez-Lozano
Download or read book Statistical Inference from High Dimensional Data written by Carlos Fernandez-Lozano and published by MDPI. This book was released on 2021-04-28 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Real-world problems can be high-dimensional, complex, and noisy • More data does not imply more information • Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information • A process with multidimensional information is not necessarily easy to interpret nor process • In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth • The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data • The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches • Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data
Book Synopsis Syntactic And Structural Pattern Recognition - Theory And Applications by : Horst Bunke
Download or read book Syntactic And Structural Pattern Recognition - Theory And Applications written by Horst Bunke and published by World Scientific. This book was released on 1990-01-01 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is currently the only one on this subject containing both introductory material and advanced recent research results. It presents, at one end, fundamental concepts and notations developed in syntactic and structural pattern recognition and at the other, reports on the current state of the art with respect to both methodology and applications. In particular, it includes artificial intelligence related techniques, which are likely to become very important in future pattern recognition.The book consists of individual chapters written by different authors. The chapters are grouped into broader subject areas like “Syntactic Representation and Parsing”, “Structural Representation and Matching”, “Learning”, etc. Each chapter is a self-contained presentation of one particular topic. In order to keep the original flavor of each contribution, no efforts were undertaken to unify the different chapters with respect to notation. Naturally, the self-containedness of the individual chapters results in some redundancy. However, we believe that this handicap is compensated by the fact that each contribution can be read individually without prior study of the preceding chapters. A unification of the spectrum of material covered by the individual chapters is provided by the subject and author index included at the end of the book.
Book Synopsis Research Papers in Statistical Inference for Time Series and Related Models by : Yan Liu
Download or read book Research Papers in Statistical Inference for Time Series and Related Models written by Yan Liu and published by Springer Nature. This book was released on 2023-05-31 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.
Author :Rafael C. Carrasco Publisher :Springer Science & Business Media ISBN 13 :9783540584735 Total Pages :308 pages Book Rating :4.5/5 (847 download)
Book Synopsis Grammatical Inference and Applications by : Rafael C. Carrasco
Download or read book Grammatical Inference and Applications written by Rafael C. Carrasco and published by Springer Science & Business Media. This book was released on 1994-09-07 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the proceedings of the Second International Colloquium on Grammatical Inference (ICGI-94), held in Alicante, Spain in September 1994. Besides 25 research papers carefully selected and refereed by the program committee, the book contains a survey by E. Vidal. The book is devoted to all those aspects of automatic learning that explicitly focus on principles, theory, and applications of grammars and languages. The papers are organized in sections on formal aspects; language modelling and linguistic applications; stochastic approaches, applications and performance analysis; and neural networks, genetic algorithms, and artificial intelligence techniques.
Book Synopsis Algorithms For Analysis, Inference, And Control Of Boolean Networks by : Tatsuya Akutsu
Download or read book Algorithms For Analysis, Inference, And Control Of Boolean Networks written by Tatsuya Akutsu and published by World Scientific. This book was released on 2018-02-14 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Boolean network (BN) is a mathematical model of genetic networks and other biological networks. Although extensive studies have been done on BNs from a viewpoint of complex systems, not so many studies have been undertaken from a computational viewpoint. This book presents rigorous algorithmic results on important computational problems on BNs, which include inference of a BN, detection of singleton and periodic attractors in a BN, and control of a BN. This book also presents algorithmic results on fundamental computational problems on probabilistic Boolean networks and a Boolean model of metabolic networks. Although most contents of the book are based on the work by the author and collaborators, other important computational results and techniques are also reviewed or explained.
