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JSS
Journal of Statistical Software
MMMMMM YYYY, Volume VV, Book Review II.
http://www.jstatsoft.org/
Reviewer: John Maindonald
Australian National University
Pattern Recognition and Machine Learning
Christopher M. Bishop
Springer, New York, 2006.
ISBN 0387310738. 738+xx pp. USD 74.95.
http://research.microsoft.com/~cmbishop/PRML
This beautifully produced book is intended for advanced undergraduates, PhD students, and
researchers and practitioners, primarily in machine learning or allied areas. The theoretical
framework is, as far as possible, that of Bayesian decision theory, taking advantage of the
computational tools now available for practical implementation of such methods. Readers
should have a good grasp of calculus and linear algebra and, preferably, some prior familiarity
with probability theory.
Two examples are used in the ﬁrst chapter for motivation – recognition of handwritten digits,
and polynomial curve ﬁtting. These typify two classes of problem which are the subject
of this book. These are: regression with a categorical outcome variable, otherwise known as
discriminant analysis or supervised classiﬁcation; and regression with a continuous or perhaps
ordinal outcome variable.
A strong feature is the use of geometric illustration and intuition, noting however that 2 or
3dimensional analogues are not always eﬀective for higher numbers of dimensions. There is
helpful commentary that explains why, e.g., linear models might be useful in one context and
neural networks in another. The discussion of Support Vector Machines notes, among other
limitations, that the generation of decision values rather than probabilities prevents use of a
Bayesion decision theoretic framework.
Chapters 1 and 2 develop Bayesian decision theory, and introduce commonly used families
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 Spring '09
 johnson
 Machine Learning

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