The Art and Science of Machine Intelligence

With An Innovative Application for Alzheimer's Detection from Speech
1. Auflage 2020
ISBN: 978-3-030-18495-7
Verlag: Springer

Buch, Englisch, 269 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 606 g

This volume presents several machine intelligence technologies, developed over recent decades, and illustrates how they can be combined in application. One application, the detection of dementia from patterns in speech, is used throughout to illustrate these combinations. This application is a classic stationary pattern detection task, so readers may easily see how these combinations can be applied to other similar tasks. The expositions of the methods are supported by the basic theory they rest upon, and their application is clearly illustrated. The book’s goal is to allow readers to select one or more of these methods to quickly apply to their own tasks.

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Includes a variety of machine intelligent technologies and illustrates how they can work together

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Shows evolutionary feature subset selection combined with support vector machines and multiple classifiers combined

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Includes a running case study on intelligent processing relating to Alzheimer’s / dementia detection, in addition to several applications of the machine hybrid algorithms

Zielgruppe

Research

Autoren/Hrsg.

Land Jr., Walker H.

Schaffer, J. David

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

Introduction.- Background for Genetic Algorithms.- Support Vector Machines (SVMs).- The Generalized Regression Neural Network (GRNN) Oracle.- Alzheimer’s Disease (AD) Background.- Genetic Algorithm (GA)-SVM Paradigm.- GA-SVM Paradigm Applied to Detecting AD from Speech.- Classical Bayesian Networks (BN) MI Developed Bayesian Networks.- Generalization of MI methods.- Selected research studies.- Conclusion.

Über Autor(innen)

Professor Walker Land Jr. performed twenty years of classified military research for IBM, none of which can be discussed here. He retired from IBM in 1990 and joined Binghamton University for 26 years, retiring a second time at the rank of Research Professor in 2014. He is known as one of the prime developers of the GRNN oracle ensemble method for learning classifier systems. He also made contributions to the original TIROS satellite system, the forerunner of the current weather satellite system, as well as made original contributions to the 200 and 500 series Saturn/Apollo lunar flyby and landing programs.

Professor J David Schaffer published many years on genetic algorithms. He retired as Research Fellow after 25 years with Philips Research. He has a citation index of 12000 (Google Scholar), holds 43 issued US patents and was designated a Pioneer in Evolutionary Computation by the IEEE Computational Intelligence Society in 2012. He serves on the editorial board of the Evolutionary Computation Journal, and on the steering committee for the bi-annual conference series Evolutionary Multiobjective Optimization (EMO).