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

"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 "This thoroughly enjoyable little book on learning theory reminds me of many of 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 between 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

The implications for philosophy and cognitive science of developments in statistical learning theory.