699488

9780262032254

Empirical Methods for Artificial Intelligence

Empirical Methods for Artificial Intelligence
$203.64
$3.95 Shipping
  • Condition: New
  • Provider: Ergodebooks Contact
  • Provider Rating:
    82%
  • Ships From: Multiple Locations
  • Shipping: Standard
  • Comments: Buy with confidence. Excellent Customer Service & Return policy. Ships Fast. 24*7 Customer Service.

   30-day money back guarantee
$108.68
$3.95 Shipping
  • Condition: Good
  • Provider: Ergodebooks Contact
  • Provider Rating:
    82%
  • Ships From: Multiple Locations
  • Shipping: Standard
  • Comments: Buy with confidence. Excellent Customer Service & Return policy. Ships Fast. 24*7 Customer Service.

   30-day money back guarantee

Ask the provider about this item.

Most renters respond to questions in 48 hours or less.
The response will be emailed to you.
Cancel
  • ISBN-13: 9780262032254
  • ISBN: 0262032252
  • Publisher: MIT Press

AUTHOR

Cohen, Paul R.

SUMMARY

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 BookCohen, Paul R. is the author of 'Empirical Methods for Artificial Intelligence' with ISBN 9780262032254 and ISBN 0262032252.

[read more]

Questions about purchases?

You can find lots of answers to common customer questions in our FAQs

View a detailed breakdown of our shipping prices

Learn about our return policy

Still need help? Feel free to contact us

View college textbooks by subject
and top textbooks for college

The ValoreBooks Guarantee

The ValoreBooks Guarantee

With our dedicated customer support team, 30-day no-questions-asked return policy, and our price match guarantee, you can rest easy knowing that we're doing everything we can to save you time, money, and stress.