Reliable Reasoning: Induction and Statistical Learning Theory (Jean Nicod Lectures) - Hardcover

Gilbert Harman; Sanjeev Kulkarni

Hardcover

ISBN 10: 0262083604 ISBN 13: 9780262083607

Publisher: Bradford Book, 2007

Synopsis

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.

About this title

Review:

--Sanjoy K. Mitter, Professor of Electrical Engineering, MIT

--Stephen J. Hanson, Department of Psychology, Rutgers University

" 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

" 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

& quot; 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 & lsquo; projecting& rsquo; 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.& quot; -- Stephen J. Hanson, Department of Psychology, Rutgers University

& quot; 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.& quot; -- Sanjoy K. Mitter, Professor of Electrical Engineering, MIT

"In their interesting and stimulating book

"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

Synopsis:

This work talks about 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, describing fundamental results about the power and limits of those methods in terms of the VC-dimension of the hypotheses being considered.

The VC-dimension is found to be superior to a related measure proposed by Karl Popper, and shown not to correspond exactly to ordinary notions of simplicity. 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.

About this edition

• Publisher: Bradford Book
• Publication date: 2007
• ISBN 10: 0262083604
• ISBN 13: 9780262083607
• Binding: Hardcover
• Edition number: 1
• Number of pages: 114