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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...

Data Mining: Foundations and Intelligent Paradigms: VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects
Data Mining: Foundations and Intelligent Paradigms: VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of...

Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications
Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains...

Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)
Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and...

Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks

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...

Thoughtful Machine Learning: A Test-Driven Approach
Thoughtful Machine Learning: A Test-Driven Approach

Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive...

Bayesian Brain: Probabilistic Approaches to Neural Coding (Computational Neuroscience)
Bayesian Brain: Probabilistic Approaches to Neural Coding (Computational Neuroscience)

A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic functioning of the brain circuit, and by suggesting optimal ways of deciphering...

Intelligent Data Analysis
Intelligent Data Analysis
This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The ten coherently written chapters by leading experts provide complete coverage of the core issues.

The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of...

Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Join author John Zdziarski for a look inside the brilliant minds that have conceived clever new ways to fight spam in all its nefarious forms. This landmark title describes, in-depth, how statistical filtering is being used by next-generation spam filters to identify and filter unwanted messages, how spam filtering works and how language...
Heuristic and Optimization for Knowledge Discovery
Heuristic and Optimization for Knowledge Discovery
With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic...
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...

Advances in Mathematical Modeling for Reliability
Advances in Mathematical Modeling for Reliability
Advances in Mathematical Modeling for Reliability discusses fundamental issues on mathematical modeling in reliability theory and its applications. Beginning with an extensive discussion of graphical modeling and Bayesian networks, the focus shifts towards repairable systems: a discussion about how sensitive availability calculations parameter...
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