Download or read book Introduction to Hidden Markov Models and Their Applications to Classification Problems written by Michail Zambartas and published by . This book was released on 1999 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an introduction to Hidden Markov models (HMM) and their applications to classification problems. HMMs have been used extensively to model the temporal structure and variability of speech and other signals in the last decade. We selected to write our own HMM implementation in MATLAB. We tested our software on a limited isolated 4-word recognition. We also applied our implementation to the recognition of mine-like objects buried in shallow sand, using seismo-acoustic data obtained from an on-going project at the Naval Postgraduate School. Initial results indicate that the HMM-based classifier can recognize the type of mine-like object, independent of the object weight with a 97% accuracy. Results also indicate that it can recognize the object type at different distances with a 100% accuracy. However, the experiments were conducted with very few data, and further work needs to be done to confirm these initial findings by using a larger data set. Finally, we benchmarked our results against those obtained using a back-propagation neural network implementation, which were found to be similar, but slower than the HMM- based implementation.
Download or read book Induction to Hidden Markov Models and Their Applications to Classification Problems written by Michail Zambartas and published by . This book was released on 1999-09-01 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an introduction to Hidden Markov models (HMM) and their applications to classification problems. HMMs have been used extensively to model the temporal structure and variability of speech and other signals in the last decade. We selected to write our own HMM implementation in MATLAB. We tested our software on a limited isolated 4-word recognition. We also applied our implementation to the recognition of mine-like objects buried in shallow sand, using seismo-acoustic data obtained from an on-going project at the Naval Postgraduate School. Initial results indicate that the HMM-based classifier can recognize the type of mine-like object, independent of the object weight with a 97% accuracy. Results also indicate that it can recognize the object type at different distances with a 100% accuracy. However, the experiments were conducted with very few data, and further work needs to be done to confirm these initial findings by using a larger data set. Finally, we benchmarked our results against those obtained using a back-propagation neural network implementation, which were found to be similar, but slower than the HMM- based implementation.
Download or read book Hidden Markov Models and Applications written by Nizar Bouguila and published by Springer Nature. This book was released on 2022-05-19 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.
Download or read book Hidden Markov Models for Time Series written by Walter Zucchini and published by CRC Press. This book was released on 2017-12-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data
Download or read book Markov Models written by Steven Taylor and published by Steven Taylor. This book was released on 2020-07-14 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Models This book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling. Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital as well as when events occur more than once. This book highlights several applications of Markov Models. Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits. By Downloading This Book Now You Will Discover: Hidden Markov Models Dynamic Bayesian Networks Stepwise Mutations using the Wright Fisher Model Using Normalized Algorithms to Update the Formulas Types of Markov Processes Important Tools used with HMM Machine Learning And much much more! Download this book now and learn more about Markov Models!
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 Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
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 Hidden Markov Models written by Przemyslaw Dymarski and published by BoD – Books on Demand. This book was released on 2011-04-19 with total page 329 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 Hidden Markov Models Applications In Computer Vision written by Horst Bunke and published by World Scientific. This book was released on 2001-06-04 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).
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 2018-02-08 with total page 186 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 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 Hidden Markov and Other Models for Discrete valued Time Series written by Iain L. MacDonald and published by CRC Press. This book was released on 1997-01-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.
Download or read book Latent Markov Models for Longitudinal Data written by Francesco Bartolucci and published by CRC Press. This book was released on 2012-10-29 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing on the authors' extensive research in the analysis of categorical longitudinal data, this book focuses on the formulation of latent Markov models and the practical use of these models. It demonstrates how to use the models in three types of analysis, with numerous examples illustrating how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB routines used for the examples are available on the authors' website.
Download or read book Generalized Multi stream Hidden Markov Models written by Oualid Missaoui and published by . This book was released on 2010 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: For complex classification systems, data is usually gathered from multiple sources of information that have varying degree of reliability. In fact, assuming that the different sources have the same relevance in describing all the data might lead to an erroneous behavior. The classification error accumulates and can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is compelling evidence that learning algorithms should include a relevance weight for each source of information (stream) as a parameter that needs to be learned. In this dissertation, we assumed that the multi-stream temporal data is generated by independent and synchronous streams. Using this assumption, we develop, implement, and test multi- stream continuous and discrete hidden Markov model (HMM) algorithms. For the discrete case, we propose two new approaches to generalize the baseline discrete HMM. The first one combines unsupervised learning, feature discrimination, standard discrete HMMs and weighted distances to learn the codebook with feature-dependent weights for each symbol. The second approach consists of modifying the HMM structure to include stream relevance weights, generalizing the standard discrete Baum-Welch learning algorithm, and deriving the necessary conditions to optimize all model parameters simultaneously. We also generalize the minimum classification error (MCE) discriminative training algorithm to include stream relevance weights. For the continuous HMM, we introduce a. new approach that integrates the stream relevance weights in the objective function. Our approach is based on the linearization of the probability density function. Two variations are proposed: the mixture and state level variations. As in the discrete case, we generalize the continuous Baum-Welch learning algorithm to accommodate these changes, and we derive the necessary conditions for updating the model parameters. We also generalize the MCE learning algorithm to derive the necessary conditions for the model parameters' update. The proposed discrete and continuous HMM are tested on synthetic data sets. They are also validated on various applications including Australian Sign Language, audio classification, face classification, and more extensively on the problem of landmine detection using ground penetrating radar data. For all applications, we show that considerable improvement can be achieved compared to the baseline HMM and the existing multi-stream HMM algorithms.
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 Biological Sequence Analysis written by Richard Durbin and published by Cambridge University Press. This book was released on 1998-04-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
