Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images

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Publisher : Springer Nature
ISBN 13 : 3031553896
Total Pages : 372 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images by :

Download or read book Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images written by and published by Springer Nature. This book was released on 2024 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive presentation of a recently introduced framework, named "probabilistic indexing" (PrIx), for searching text in large collections of document images and other related applications. It fosters the development of new search engines for effective information retrieval from manuscripts which, however, lack the electronic text (transcripts) that would typically be required for such search and retrieval tasks. The book is structured into 11 chapters and three appendices. The first two chapters briefly outline the necessary fundamentals and state of the art in pattern recognition, statistical decision theory, and handwritten text recognition. Chapter 3 presents approaches for indexing (as opposed to spotting) each region of a handwritten text image which is likely to contain a word. Next, Chapter 4 describes models adopted for handwritten text in images, namely hidden Markov models, convolutional and recurrent neural networks and language models, and provides full details of weighted finite-state transducer (WFST) concepts and methods, needed in further chapters of the book. Chapter 5 explains the set of techniques and algorithms developed to generate image probabilistic indexes which allow for fast search and retrieval of textual information in the indexed images. Chapter 6 then presents experimental evaluations of the proposed framework and algorithms on different traditional benchmark datasets and compares them with other approaches, while Chapter 7 reviews the most popular keyword-spotting approaches. Chapter 8 explains how PrIx can support classical free-text search tools, while Chapter 9 presents new methods that use PrIx not only for searching, but also to deal with text analytics and other related natural language processing and information extraction tasks. Chapter 10 shows how the proposed solutions can be used to effectively index very large collections of handwritten document images, before Chapter 11 eventually summarizes the book and suggests promising lines of future research. The appendices detail the necessary mathematical foundations for the work and presents details of the text image collections and datasets used in the experiments throughout the book. This book is written for researchers and (post-)graduate students in pattern recognition and information retrieval. It will also be of interest to people in areas like history, criminology, or psychology who need technical support to evaluate, understand or decode historical or contemporary handwritten text.

Probabilistic Databases

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Publisher : Springer Nature
ISBN 13 : 3031018796
Total Pages : 164 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Probabilistic Databases by : Dan Suciu

Download or read book Probabilistic Databases written by Dan Suciu and published by Springer Nature. This book was released on 2022-05-31 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques

Readings in Information Retrieval

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Publisher : Morgan Kaufmann
ISBN 13 : 9781558604544
Total Pages : 614 pages
Book Rating : 4.6/5 (45 download)

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Book Synopsis Readings in Information Retrieval by : Karen Sparck Jones

Download or read book Readings in Information Retrieval written by Karen Sparck Jones and published by Morgan Kaufmann. This book was released on 1997 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compilation of original papers on information retrieval presents an overview, covering both general theory and specific methods, of the development and current status of information retrieval systems. Each chapter contains several papers carefully chosen to represent substantive research work that has been carried out in that area, each is preceded by an introductory overview and followed by supported references for further reading.

Probability and Computing

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Publisher : Cambridge University Press
ISBN 13 : 9780521835404
Total Pages : 372 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Probability and Computing by : Michael Mitzenmacher

Download or read book Probability and Computing written by Michael Mitzenmacher and published by Cambridge University Press. This book was released on 2005-01-31 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This 2005 textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, the probabilistic method and Markov chains. The second half covers more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.

Probabilistic Machine Learning

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Publisher : MIT Press
ISBN 13 : 0262369303
Total Pages : 858 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Probabilistic Linguistics

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Author :
Publisher : A Bradford Book
ISBN 13 : 0262025361
Total Pages : 465 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Probabilistic Linguistics by : Rens Bod

Download or read book Probabilistic Linguistics written by Rens Bod and published by A Bradford Book. This book was released on 2003-04-08 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the past forty years, linguistics has been dominated by the idea that language is categorical and linguistic competence discrete. It has become increasingly clear, however, that many levels of representation, from phonemes to sentence structure, show probabilistic properties, as does the language faculty. Probabilistic linguistics conceptualizes categories as distributions and views knowledge of language not as a minimal set of categorical constraints but as a set of gradient rules that may be characterized by a statistical distribution. Whereas categorical approaches focus on the endpoints of distributions of linguistic phenomena, probabilistic approaches focus on the gradient middle ground. Probabilistic linguistics integrates all the progress made by linguistics thus far with a probabilistic perspective. This book presents a comprehensive introduction to probabilistic approaches to linguistic inquiry. It covers the application of probabilistic techniques to phonology, morphology, semantics, syntax, language acquisition, psycholinguistics, historical linguistics, and sociolinguistics. It also includes a tutorial on elementary probability theory and probabilistic grammars.

Introduction to Information Retrieval

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Author :
Publisher : Cambridge University Press
ISBN 13 : 1139472100
Total Pages : pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Introduction to Information Retrieval by : Christopher D. Manning

Download or read book Introduction to Information Retrieval written by Christopher D. Manning and published by Cambridge University Press. This book was released on 2008-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Probabilistic Databases

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608456803
Total Pages : 183 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Probabilistic Databases by : Dan Suciu

Download or read book Probabilistic Databases written by Dan Suciu and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques

Advances in Probabilistic Graphical Models

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Publisher : Springer
ISBN 13 : 3540689966
Total Pages : 386 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Advances in Probabilistic Graphical Models by : Peter Lucas

Download or read book Advances in Probabilistic Graphical Models written by Peter Lucas and published by Springer. This book was released on 2007-06-12 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

Computer Literature Bibliography: 1946-1963

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Publisher :
ISBN 13 :
Total Pages : 476 pages
Book Rating : 4.:/5 (321 download)

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Book Synopsis Computer Literature Bibliography: 1946-1963 by : W. W. Youden

Download or read book Computer Literature Bibliography: 1946-1963 written by W. W. Youden and published by . This book was released on 1965 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Probabilistic Transmission System Planning

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Publisher : John Wiley & Sons
ISBN 13 : 0470934700
Total Pages : 276 pages
Book Rating : 4.4/5 (79 download)

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Book Synopsis Probabilistic Transmission System Planning by : Wenyuan Li

Download or read book Probabilistic Transmission System Planning written by Wenyuan Li and published by John Wiley & Sons. This book was released on 2011-10-14 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is composed of 12 chapters and three appendices, and can be divided into four parts. The first part includes Chapters 2 to 7, which discuss the concepts, models, methods and data in probabilistic transmission planning. The second part, Chapters 8 to 11, addresses four essential issues in probabilistic transmission planning applications using actual utility systems as examples. Chapter 12, as the third part, focuses on a special issue, i.e. how to deal with uncertainty of data in probabilistic transmission planning. The fourth part consists of three appendices, which provide the basic knowledge in mathematics for probabilistic planning.

Decision Support for Information Indexing and Retrieval

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Publisher :
ISBN 13 :
Total Pages : 260 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Decision Support for Information Indexing and Retrieval by : Wenli Zhu

Download or read book Decision Support for Information Indexing and Retrieval written by Wenli Zhu and published by . This book was released on 1995 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Web Information Retrieval

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Publisher : Springer Science & Business Media
ISBN 13 : 3642393144
Total Pages : 287 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Web Information Retrieval by : Stefano Ceri

Download or read book Web Information Retrieval written by Stefano Ceri and published by Springer Science & Business Media. This book was released on 2013-08-30 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the proliferation of huge amounts of (heterogeneous) data on the Web, the importance of information retrieval (IR) has grown considerably over the last few years. Big players in the computer industry, such as Google, Microsoft and Yahoo!, are the primary contributors of technology for fast access to Web-based information; and searching capabilities are now integrated into most information systems, ranging from business management software and customer relationship systems to social networks and mobile phone applications. Ceri and his co-authors aim at taking their readers from the foundations of modern information retrieval to the most advanced challenges of Web IR. To this end, their book is divided into three parts. The first part addresses the principles of IR and provides a systematic and compact description of basic information retrieval techniques (including binary, vector space and probabilistic models as well as natural language search processing) before focusing on its application to the Web. Part two addresses the foundational aspects of Web IR by discussing the general architecture of search engines (with a focus on the crawling and indexing processes), describing link analysis methods (specifically Page Rank and HITS), addressing recommendation and diversification, and finally presenting advertising in search (the main source of revenues for search engines). The third and final part describes advanced aspects of Web search, each chapter providing a self-contained, up-to-date survey on current Web research directions. Topics in this part include meta-search and multi-domain search, semantic search, search in the context of multimedia data, and crowd search. The book is ideally suited to courses on information retrieval, as it covers all Web-independent foundational aspects. Its presentation is self-contained and does not require prior background knowledge. It can also be used in the context of classic courses on data management, allowing the instructor to cover both structured and unstructured data in various formats. Its classroom use is facilitated by a set of slides, which can be downloaded from www.search-computing.org.

A Comparison of Probabilistic Unfolding Theories for Paired Comparisons Data

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Publisher : Springer Science & Business Media
ISBN 13 : 3642841724
Total Pages : 242 pages
Book Rating : 4.6/5 (428 download)

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Book Synopsis A Comparison of Probabilistic Unfolding Theories for Paired Comparisons Data by : Patrick Bossuyt

Download or read book A Comparison of Probabilistic Unfolding Theories for Paired Comparisons Data written by Patrick Bossuyt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some data-analytic methods excel by their sheer elegance. Their basic principles seem to have a particular attraction, based on a intricate combination of simplicity, deliberation, and power. They usually balance on the verge of two disciplines, data-analysis and foundational measurement, or statistics and psychology. To me, unfolding has always been one of them. The theory and the original methodology were created by Clyde Coombs (1912-1988) to describe and analyze preferential choice data. The fundamental assumptions are truly psy chological; Unfolding is based on the notion of a single peaked preference function over a psychological similarity space, or, in an alternative but equivalent expression, on the assumption of implicit comparisons with an ideal alternative. Unfolding has proved to be a very constructive data-analytic principle, and a source of inspiration for many theories on choice behavior. Yet the number of applications has not lived up to the acclaim the theory has received among mathematical psychologists. One of the reasons is that it requires far more consistency in human choice behavior than can be expected. Several authors have tried to attenuate these requirements by turning the deterministic unfolding theory into a probabilistic one. Since Coombs first put forth a probabilistic version of his theory, a number of competing proposals have been presented in the literature over the past thirty years. This monograph contains a summary and a comparison of unfolding theories for paired comparisons data, and an evaluation strategy designed to assess the validity of these theories in empirical choice tasks.

Information Retrieval Models

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Publisher : Springer Nature
ISBN 13 : 3031023285
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Information Retrieval Models by : Thomas Roelleke

Download or read book Information Retrieval Models written by Thomas Roelleke and published by Springer Nature. This book was released on 2022-05-31 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index

Proceedings of the 6th International Probabilistic Workshop

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Publisher : Dirk Proske Verlag
ISBN 13 : 3000250506
Total Pages : 568 pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis Proceedings of the 6th International Probabilistic Workshop by : Carl-Alexander Graubner

Download or read book Proceedings of the 6th International Probabilistic Workshop written by Carl-Alexander Graubner and published by Dirk Proske Verlag. This book was released on 2008 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Intelligent Data Analysis

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Publisher : Springer Science & Business Media
ISBN 13 : 3540663320
Total Pages : 529 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Advances in Intelligent Data Analysis by : David Hand

Download or read book Advances in Intelligent Data Analysis written by David Hand and published by Springer Science & Business Media. This book was released on 1999-07-28 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Symposium on Intelligent Data Analysis, IDA-99 held in Amsterdam, The Netherlands in August 1999. The 21 revised full papers and 23 posters presented in the book were carefully reviewed and selected from a total of more than 100 submissions. The papers address all current aspects of intelligent data analysis; they are organized in sections on learning, visualization, classification and clustering, integration, applications and media mining.