Empirical Methods for Artificial Intelligence (MIT Press)

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9780262032254: Empirical Methods for Artificial Intelligence (MIT Press)

This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data.

Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.

Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods -- which deal with specific systems -- can foster general theories. Mathematical details are confined to appendixes and no prior knowledge of statistics or probability theory is assumed. All of the examples can be analyzed by hand or with commercially available statistics packages.

The Common Lisp Analytical Statistics Package (CLASP), developed in the author's laboratory for Unix and Macintosh computers, is available from The MIT Press.

A Bradford Book

"Sinopsis" puede pertenecer a otra edición de este libro.

About the Author:

Paul Cohen is Professor in the Department of Computer Science at the University of Massachusetts at Amherst.

"Sobre este título" puede pertenecer a otra edición de este libro.

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Cohen, Paul R.
Editorial: A Bradford Book (1995)
ISBN 10: 0262032252 ISBN 13: 9780262032254
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Descripción A Bradford Book, 1995. Hardcover. Estado de conservación: New. Never used!. Nº de ref. de la librería P110262032252

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Cohen, Paul R. (Beal and Cohen)
Editorial: MIT Press (1995)
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Descripción MIT Press, 1995. HRD. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería WM-9780262032254

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Paul R. Cohen
Editorial: MIT Press Ltd, United States (1995)
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Descripción MIT Press Ltd, United States, 1995. Hardback. Estado de conservación: New. Language: English . Brand New Book. Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods -- which deal with specific systems -- can foster general theories. Mathematical details are confined to appendixes and no prior knowledge of statistics or probability theory is assumed. All of the examples can be analyzed by hand or with commercially available statistics packages.The Common Lisp Analytical Statistics Package (CLASP), developed in the author s laboratory for Unix and Macintosh computers, is available from The MIT Press.A Bradford Book. Nº de ref. de la librería AAH9780262032254

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Paul R. Cohen
Editorial: MIT Press Ltd, United States (1995)
ISBN 10: 0262032252 ISBN 13: 9780262032254
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Descripción MIT Press Ltd, United States, 1995. Hardback. Estado de conservación: New. Language: English . Brand New Book. Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods -- which deal with specific systems -- can foster general theories. Mathematical details are confined to appendixes and no prior knowledge of statistics or probability theory is assumed. All of the examples can be analyzed by hand or with commercially available statistics packages.The Common Lisp Analytical Statistics Package (CLASP), developed in the author s laboratory for Unix and Macintosh computers, is available from The MIT Press.A Bradford Book. Nº de ref. de la librería AAH9780262032254

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Paul R. Cohen
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Descripción MIT Press. Hardcover. Estado de conservación: New. New copy - Usually dispatched within 2 working days. Nº de ref. de la librería B9780262032254

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Descripción A Bradford Book, 1995. Hardcover. Estado de conservación: New. Nº de ref. de la librería DADAX0262032252

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Descripción Bradford Books, 1995. Hardcover. Estado de conservación: Brand New. first edition edition. 421 pages. 9.50x8.75x1.25 inches. In Stock. Nº de ref. de la librería __0262032252

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Descripción Estado de conservación: New. Depending on your location, this item may ship from the US or UK. Nº de ref. de la librería 97802620322540000000

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