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Reliable Reasoning: Induction and Statistical Learning Theory (Jean Nicod Lectures) Hardcover – March 30, 2007

ISBN-13: 978-0262083607 ISBN-10: 0262083604 Edition: 1st

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Product Details

  • Series: Jean Nicod Lectures
  • Hardcover: 118 pages
  • Publisher: A Bradford Book; 1 edition (March 30, 2007)
  • Language: English
  • ISBN-10: 0262083604
  • ISBN-13: 978-0262083607
  • Product Dimensions: 5.4 x 0.2 x 8 inches
  • Shipping Weight: 8.8 ounces (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #3,620,368 in Books (See Top 100 in Books)

Editorial Reviews

Review

In their interesting and stimulating book Reliable Reasoning, Harman, a philosopher, and Kulkarni, an information scientist, illuminate the philosophical issues related to inductive reasoning by studying it in terms of the mathematics of probabilistic learning. One of the great virtues of this approach is that the inductive inference made through learning can survive changes in the probabilistic modeling assumptions. I find that the authors have made a convincing and persuasive case for rigorously studying the philosophical issues related to inductive inference using recent ideas from the science of artificial intelligence.

(Sanjoy K. Mitter, Professor of Electrical Engineering, MIT)

This thoroughly enjoyable little book on learning theory reminds me of many classics in the field, such as Nilsson's Learning Machines or Minksy and Papert's Perceptrons: It is both a concise and timely tutorial 'projecting' the last decade of complex learning issues into simple and comprehensible forms and a vehicle for exciting new links among cognitive science, philosophy, and computational complexity.

(Stephen J. Hanson, Department of Psychology, Rutgers University)

About the Author

Gilbert Harman is Stuart Professor of Philosophy at Princeton University and the author of Explaining Value and Other Essays in Moral Philosophy and Reasoning, Meaning, and Mind.

Sanjeev Kulkarni is Professor of Electrical Engineering and an associated faculty member of the Department of Philosophy at Princeton University with many publications in statistical learning theory.

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Most Helpful Customer Reviews

5 of 5 people found the following review helpful By wolvie05 VINE VOICE on June 6, 2007
Format: Hardcover
I had the great priviledge of taking the class upon which this book was based last semester at Princeton University under professors Harman and Kulkarni. It is a fascinating little book, which manages to distill decades of debate and research into concise, readable chapters that carry the presentation forward. The authors' approach is original but commonsensical and they clearly demonstrate the value of interdisciplinary work in their twin fields of philosophy and electrical engineering!

The book is not without its flaws, however. The first chapter seems to take off 'in medias res' expecting the reader to be fully caught up with the latest discussion on the problem of induction, and it is not always clear exactly what a 'process of reasoning' might be compared to deductive arguments. The discussion could have benefited from incorporating material from the other draft textbook we used in class, on "The Nature and Limits of Learning", and even from the lecture handouts. The discussion of simplicity, as well, could have been clarified, especially with regard to Goodman's new riddle of induction and Karl Popper's philosophy of science.

Also rather disappointing in class was the discovery that Harman and Kulkarni's method do not warrant going beyond instrumentalism in scientific theorizing. I was hoping for something a little more robust. In any case, this book should be read by anyone interested in the issues they raise. It sure got me thinking and I will definitely refer to it later on as my research in philosophy brings me in contact again with the issues they discuss.
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By Jonathan Kane on September 20, 2014
Format: Paperback Verified Purchase
Good attempt to combine inductive reasoning, philosophy, psychology, and machine learning.
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