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Books > Humanities > Philosophy > Topics in philosophy > Philosophy of mind
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Reliable Reasoning - Induction and Statistical Learning Theory (Paperback)
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Reliable Reasoning - Induction and Statistical Learning Theory (Paperback)
Series: Jean Nicod Lectures
Expected to ship within 18 - 22 working days
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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.
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