Download or read book Markov Models and Linguistic Theory written by Friederick J. Damerau and published by Walter de Gruyter GmbH & Co KG. This book was released on 2018-12-03 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: No detailed description available for "Markov Models and Linguistic Theory".
Download or read book Markov Models and Linguistic Theory written by Frederick J. Damerau and published by . This book was released on 1971 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Role of Markov Models in Linguistic Theory written by Frederick Jacob Damerau and published by . This book was released on 1900 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Role of Markov Models in Linguistic Theory written by Frederick Jacob Damerau and published by . This book was released on 1982 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The role of Markov models in linguistic theory written by Frederick J. Damerau and published by . This book was released on 1979 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Hidden Markov Models written by João Paulo Coelho and published by CRC Press. This book was released on 2019-08-02 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach. Key Selling Points: Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory Covers the analysis of both continuous and discrete Markov chains Discusses the translation of HMM concepts from the realm of formal mathematics into computer code Offers many examples to supplement mathematical notation when explaining new concepts
Download or read book Markov Models for Pattern Recognition written by Gernot A. Fink and published by Springer Science & Business Media. This book was released on 2014-01-14 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.
Download or read book Computational Cognitive Modeling and Linguistic Theory written by Adrian Brasoveanu and published by Springer Nature. This book was released on 2020-01-01 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data. This computational cognitive modeling approach opens up exciting new directions for research in formal semantics, and linguistics more generally, and offers new ways of (re)connecting semantics and the broader field of cognitive science. The approach of this book is novel in more ways than one. Assuming the mental architecture and procedural modalities of Anderson's ACT-R framework, it presents fine-grained computational models of human language processing tasks which make detailed quantitative predictions that can be checked against the results of self-paced reading and other psycho-linguistic experiments. All models are presented as computer programs that readers can run on their own computer and on inputs of their choice, thereby learning to design, program and run their own models. But even for readers who won't do all that, the book will show how such detailed, quantitatively predicting modeling of linguistic processes is possible. A methodological breakthrough and a must for anyone concerned about the future of linguistics! (Hans Kamp) This book constitutes a major step forward in linguistics and psycholinguistics. It constitutes a unique synthesis of several different research traditions: computational models of psycholinguistic processes, and formal models of semantics and discourse processing. The work also introduces a sophisticated python-based software environment for modeling linguistic processes. This book has the potential to revolutionize not only formal models of linguistics, but also models of language processing more generally. (Shravan Vasishth) .
Download or read book The Balancing Act written by Judith L. Klavans and published by MIT Press. This book was released on 1996 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Symbolic and statistical approaches to language have historically been at odds--the former viewed as difficult to test and therefore perhaps impossible to define, and the latter as descriptive but possibly inadequate. At the heart of the debate are fundamental questions concerning the nature of language, the role of data in building a model or theory, and the impact of the competence-performance distinction on the field of computational linguistics. Currently, there is an increasing realization in both camps that the two approaches have something to offer in achieving common goals. The eight contributions in this book explore the inevitable "balancing act" that must take place when symbolic and statistical approaches are brought together--including basic choices about what knowledge will be represented symbolically and how it will be obtained, what assumptions underlie the statistical model, what principles motivate the symbolic model, and what the researcher gains by combining approaches. The topics covered include an examination of the relationship between traditional linguistics and statistical methods, qualitative and quantitative methods of speech translation, study and implementation of combined techniques for automatic extraction of terminology, comparative analysis of the contributions of linguistic cues to a statistical word grouping system, automatic construction of a symbolic parser via statistical techniques, combining linguistic with statistical methods in automatic speech understanding, exploring the nature of transformation-based learning, and a hybrid symbolic/statistical approach to recovering from parser failures.
Download or read book The Application of Hidden Markov Models in Speech Recognition written by Mark Gales and published by Now Publishers Inc. This book was released on 2008 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.
Download or read book Markov Chains written by Wai-Ki Ching and published by Springer Science & Business Media. This book was released on 2013-03-27 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data. This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chain for computing the PageRank, the ranking of websites on the Internet. Chapter 3 studies Markovian models for manufacturing and re-manufacturing systems and presents closed form solutions and fast numerical algorithms for solving the captured systems. In Chapter 4, the authors present a simple hidden Markov model (HMM) with fast numerical algorithms for estimating the model parameters. An application of the HMM for customer classification is also presented. Chapter 5 discusses Markov decision processes for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6 considers higher-order Markov chain models, particularly a class of parsimonious higher-order Markov chain models. Efficient estimation methods for model parameters based on linear programming are presented. Contemporary research results on applications to demand predictions, inventory control and financial risk measurement are also presented. In Chapter 7, a class of parsimonious multivariate Markov models is introduced. Again, efficient estimation methods based on linear programming are presented. Applications to demand predictions, inventory control policy and modeling credit ratings data are discussed. Finally, Chapter 8 re-visits hidden Markov models, and the authors present a new class of hidden Markov models with efficient algorithms for estimating the model parameters. Applications to modeling interest rates, credit ratings and default data are discussed. This book is aimed at senior undergraduate students, postgraduate students, professionals, practitioners, and researchers in applied mathematics, computational science, operational research, management science and finance, who are interested in the formulation and computation of queueing networks, Markov chain models and related topics. Readers are expected to have some basic knowledge of probability theory, Markov processes and matrix theory.
Download or read book Linguistic Structure Prediction written by Noah A. Smith and published by Springer Nature. This book was released on 2022-05-31 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Download or read book Markov Models for Handwriting Recognition written by Thomas Plötz and published by Springer Science & Business Media. This book was released on 2012-02-02 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden Markov models and Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text reviews proposed solutions in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.
Download or read book Markov Models written by Robert Tier and published by Createspace Independent Publishing Platform. This book was released on 2017-03-12 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover How to Master Unsupervised Machine Learning and Crack Some of the Greatest Data Enigmas With Markov Models! Would you like to unlock the mysteries of Data Science? Are you yearning to understand how to make educated predictions on the weather, horse races, your unborn baby's facial features, or your boss's next black mood? Would you like a guide to explain these and many other "phenomenons" in clear, easy-to-understand language? If the answer is 'yes' then you'll want to Download this book today! It's never been easier to make predictions and smart analysis with the use of Markov Models. You don't need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills and a decent knowledge of Python programming in order to solve the most perplexing problems. And if you're unfamiliar with Python programming or Machine learning, don't worry, it'll all be explained in this book. Inside this book I'm going to show you how to be a data master. You'll discover how to solve almost-unsolvable machine learning problems in no time. I'm going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across. Download This Book Today and Discover: How to program with Python The secrets behind unsupervised machine learning How to use Markov Models to master machine learning How to make predictions with Markov Models How to use Markov Chains How to use Hidden Markov Models The 3 main problems of Markov Models and how to overcome them How to use Python to find the probability of longer and more complex problems What packages to get for using Python for Markov Models How to implement HMM algorithms How to build a speech recognizer A code that will turn gibberish into understandable text How to forecast the weather The secrets behind Queueing Theory The Markov Mutation Model The Secret Structure of Google's PageRank Algorithm How to perform Google PageRank in PythonAnd much, much more! So save yourself some time and frustration trying to learning these intricate algorithms on your own. Let me help you get started quickly and easily. Download Markov Models today and Enjoy Mastering Data Science!
Download or read book Hidden Markov Models written by Przemyslaw Dymarski and published by IntechOpen. This book was released on 2011-04-19 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
Download or read book Introduction to Hidden Semi Markov Models written by John Van der Hoek and published by Cambridge University Press. This book was released on 2018 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications
Download or read book Inference in Hidden Markov Models written by Olivier Cappé and published by Springer Science & Business Media. This book was released on 2006-04-12 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
