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Reliable Reasoning: - Paperback, by Harman Gilbert; Kulkarni - Very Good

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Last updated on 22 Mar, 2025 00:32:18 GMTView all revisionsView all revisions

Item specifics

Condition
Very Good: A book that has been read and does not look new, but is in excellent condition. No ...
Book Title
Reliable Reasoning: Induction and Statistical Learning Theory (Je
ISBN
9780262517348

About this product

Product Identifiers

Publisher
MIT Press
ISBN-10
0262517345
ISBN-13
9780262517348
eBay Product ID (ePID)
111082097

Product Key Features

Number of Pages
118 Pages
Publication Name
Reliable Reasoning : Induction and Statistical Learning Theory
Language
English
Publication Year
2012
Subject
General, Cognitive Psychology & Cognition, Logic
Type
Textbook
Subject Area
Philosophy, Psychology
Author
Gilbert Harman, Sanjeev Kulkarni
Series
Jean Nicod Lectures
Format
Trade Paperback

Dimensions

Item Height
0.3 in
Item Weight
5.8 Oz
Item Length
8 in
Item Width
5.4 in

Additional Product Features

Intended Audience
Trade
Dewey Edition
22
Grade From
College Graduate Student
Illustrated
Yes
Dewey Decimal
161
Synopsis
The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning , Gilbert Harman and Sanjeev Kulkarni--a philosopher and an engineer--argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors--a central topic in SLT. 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. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT., The implications for philosophy and cognitive science of developments in statistical learning theory.
LC Classification Number
BC177.H377 2012

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