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Can I read Learning from Good and Bad Data (The Springer International Series in Engineering and Computer Science, 47) on EtoBox?
Learning from Good and Bad Data (The Springer International Series in Engineering and Computer Science, 47) by Philip D. Laird is a nonfiction available to read on EtoBox.
What is Learning from Good and Bad Data (The Springer International Series in Engineering and Computer Science, 47) about?
Learning from Good and Bad Data explains the firm theoretical foundation that underlies much of the experimental research in machine learning. While the thrust of the work is theoretical, the presentation is accessible to theorists and practitioners, specialists and nonspecialists in the rapidly developing field of machine learning. Empirical learning (learning from example) is studied mathematically in order to uncover the formal structures common to much of the artificial intelligence experimental work on the subject.
Who reads Learning from Good and Bad Data (The Springer International Series in Engineering and Computer Science, 47)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Philip D. Laird
- Publisher
- Kluwer Academic Publishers
- Published
- 1988
- Language
- EN
- ISBN
- 9781461289517
- Category
- nonfiction
- Subjects
- Science, Education, Computer Science
Other editions & translations
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