This text demonstrates how to extract knowledge by finding meaningful connections among data spread throughout the Web. Readers learn methods and algorithms from the fields of information retrieval, machine learning, and data mining which, when combined, provide a solid...
Text-to-Speech Synthesis provides a complete, end-to-end account of the process of generating speech by computer. Giving an in-depth explanation of all aspects of current speech synthesis technology, it assumes no specialized prior knowledge. Introductory chapters on linguistics, phonetics, signal processing and speech signals lay the...
This book is meant for research scholars, scientists and practitioners involved with
the application of computer-based systems in critical applications. Ensuring
dependability of systems used in critical applications is important due to the
impact of their failures on human life, investment and environment. The individual
aspects of...
Critically acclaimed text for computer performance analysis—now in its second edition
The Second Edition of this now-classic text provides a current and thorough treatment of queueing systems, queueing networks, continuous and discrete-time Markov chains, and simulation. Thoroughly updated with...
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement...
GET FULLY UP-TO-DATE ON BIOINFORMATICS-THE TECHNOLOGY OF THE 21ST CENTURY
Bioinformatics showcases the latest developments in the field along with all the foundational information you'll need. It provides in-depth coverage of a wide range of autoimmune disorders and detailed analyses of suffix trees, plus late-breaking...
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical...
This textbook provides a comprehensive introduction to the theories, techniques and applications of image fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science. It should also be useful to practicing engineers who wish to learn the concepts of image fusion and use them in...
Bayesian networks have received a lot of attention over the last few decades from both scientists and engineers, and across a number of fields, including artificial intelligence (AI), statistics, cognitive science, and philosophy.
Perhaps the largest impact that Bayesian networks have had is on the field of AI, where they were...
Reveals How HMMs Can Be Used as General-Purpose Time Series Models
Implements all methods in R Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and...
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order...
