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Information Theory, Inference and Learning Algorithms

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology;

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Algorithmic Learning Theory

complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization;

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Computer Science - Theory and Applications

; database theory; distributed computing; fundamentals of machine learning, including learning theory, grammatical inference and neural computing;

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Inference in Hidden Markov Models

This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics;

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Signal Processing for Cognitive Radios

theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation;

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Information Theory and Statistics

Information Theory and Statistics is concerned with applications of information theory concepts in statistics, in the finite alphabet;

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Grammatical Inference

complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an;

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BAYESIAN INFERENCE

, utility theory and computational theory. It is organized into seven sections: foundations, information theory and prior distributions; robustness;

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Neural Information Processing

secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning;

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Neural Information Processing

secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning;

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Graphical Models for Machine Learning and Digital Communication

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this;

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Modeling and Reasoning with Bayesian Networks

, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both;

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Modeling and Reasoning with Bayesian Networks

, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both;

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Bayesian Programming

principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from;

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Bayesian Programming

principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from;

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Language and Automata Theory and Applications

This book constitutes the refereed proceedings of the 10th International Conference on Language and Automata Theory and Applications, LATA;

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Language and Automata Theory and Applications

This book constitutes the refereed proceedings of the 12th International Conference on Language and Automata Theory and Applications, LATA;

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Big Data in Omics and Imaging, Two Volume Set

theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and;

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Frontiers in Algorithmics and Algorithmic Aspects in Information and Management

networks and optimization, computational learning theory, knowledge discovery, and data mining, experimental algorithmic methodologies;

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Computing and Combinatorics

to algorithms and complexity; complexity theory; computational learning theory and knowledge discovery; cryptography, reliability and;

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Computing and Combinatorics

algorithms; automata, languages, logic and computability; complexity theory; computational learning theory; cryptography, reliability and security;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal;

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Machine Learning Theoretical Foundations and Practical Applications

learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web;

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Grammatical Inference

, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory;

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Information, Inference and Decision

Under the title 'Information, Inference and Decision' this volume in the Theory and Decision Library presents some papers on issues from;

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Pattern Recognition and Machine Learning

distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts;

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