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The implications for philosophy and cognitive science of developments in statistical learning theory. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning.
They explain the Vapnik-Chervonenkis VC dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines.
Prix et Conférences Jean Nicod (2005)
Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT. Sponsored Products are advertisements for products sold by merchants on Amazon. When you click on a Sponsored Product ad, you will be taken to an Amazon detail page where you can learn more about the product and purchase it. To learn more about Amazon Sponsored Products, click here. One of the great virtues of this approach is that the inductive inference made through learning can survive changes in the probabilistic modeling assumptions.
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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. 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.
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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 between cognitive science, philosophy, and computational complexity. 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.
G. HARMAN () - INSTITUT JEAN NICOD
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: Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support? Learn more about Amazon Prime.
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Reasoning, Meaning, and Mind. Principles of Reasoning A Bradford Book. Customers who bought this item also bought. The Book of Why: The New Science of Cause and Effect. Elements of Causal Inference: Ten Great Ideas about Chance. Category Theory in Context Aurora: Dover Modern Math Originals. Gilbert Harman will be awarded the Jean-Nicod Prize after the lecture.
Reliable Reasoning: Induction and Statistical Learning Theory
Tuesday May, 31th, 2: Thursday June, 2nd, 2: Amphitheater Going Beyond Enumerative Induction A different kind of induction balances data-coverage against something like simplicity. Martina Orlandi rated it liked it Feb 22, J Parker marked it as to-read Jul 23, Wenke Liu added it Jan 04, Frank Spencer marked it as to-read Feb 12, Shawn marked it as to-read Aug 17, Leni marked it as to-read Jun 11, Cameron Davis marked it as to-read Jul 13, Irene marked it as to-read Aug 08, Hadrian marked it as to-read Apr 03, Royce marked it as to-read Apr 04, Alex Dabell marked it as to-read Apr 05, Pavel Surmenok marked it as to-read Oct 17, Vadim marked it as to-read Oct 18, Ali marked it as to-read Nov 06, Ramzifu marked it as to-read Mar 05, Marcus Aurelius marked it as to-read May 25, Jacob Buchholz marked it as to-read May 30, Robert is currently reading it Aug 08, Gabriel Esteves is currently reading it Jan 17, There are no discussion topics on this book yet.
Other books in the series. Jean Nicod Lectures 1 - 10 of 12 books. Books by Gilbert Harman.
- I Think Im a Ghost, Sir!.
- Des clientes pour la morgue (SAN ANTONIO) (French Edition)!
- As The Seed Is Sown;
- Reliable Reasoning: Induction and Statistical Learning Theory by Gilbert Harman.
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