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Empirical Methods for Artificial Intelligence (MIT Press) download epub

by Paul R. Cohen


Epub Book: 1678 kb. | Fb2 Book: 1562 kb.

This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in. .Empirical Methods for Artificial Intelligence.

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.

Empirical Methods for Artificial Intelligence. 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.

Exploratory Data Analysis. conditions it was necessary to test KOSO and KOSO empirically. Evaluation begins with claims

Exploratory Data Analysis. Bootstrap, randomization, other Monte. Carlo sampling methods. Evaluation begins with claims. Hypothesis (or claim): KOSO takes longer than KOSO. KOSO balances loads better. The because phrase indicates a hypothesis about.

being meaningful or useful. Lesson 1: Evaluation begins with claims.

A Survey of the Eighth National Conference on Artificial Intelligence: Pulling together or pulling apart? AI Magazine, 12(1), 16-41. Exploratory Data Analysis Experiment design Hypothesis testing Bootstrap, randomization, other Monte Carlo sampling methods Simple effects Interaction effects, explaining effects Modeling Generalization. This tutorial is organized around seven lessons. being meaningful or useful.

The workshop was organized by Luc Steels and Rodney. The workshop was organized by Luc Steels and Rodney Brooks and sponsored by NATO and MIT.

Artificial intelligence certificate online or even a degree below. The Top 50 greatest fiction books of all time determined by 119 lists and articles from various critics, authors and experts.

FREE shipping on qualifying offers. Artificial intelligence certificate online or even a degree below. After you can read our article on AI. Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp. PR Cohen, R Schrag, E Jones, A Pease, A Lin, B Starr, D Gunning,. Ai Magazine 19 (4), 25-25, 1998. Emotional influences in memory and thinking: Data and theory. Affect and cognition 1, 1982. The handbook of artificial intelligence. PR Cohen, EA Feigenbaum. Heuristic reasoning about uncertainty: an artificial intelligence approach. of Massachusetts, 1985.

The title of the book could have been easily "Empirical Methods for Computer Science" or even "Empirical Methods for Science. First four chapters are really a must-read for anyone who is interested in empirical methods.

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,.

Published online by Cambridge University Press: 01 November 1998. Recommend this journal. Export citation Request permission.

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


Empirical Methods for Artificial Intelligence (MIT Press) download epub
Computer Science
Author: Paul R. Cohen
ISBN: 0262032252
Category: Computers & Technology
Subcategory: Computer Science
Language: English
Publisher: A Bradford Book (August 3, 1995)
Pages: 422 pages