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Phase Transitions in Machine Learning
Phase transitions typically occur putational problems and
have important consequences, especially with the current spread of statistical
relational learning and of sequence learning methodologies. In Phase Transi-
tions in Machine Learning the authors begin by describing in detail this phe-
nomenon and the extensive experimental investigation that supports its presence.
They then turn their attention to the possible implications and explore appropri-
ate methods for tackling them.
Weaving together fundamental aspects puter science, statistical
physics, and machine learning, the book provides sufficient mathematics and
physics background to make the subject intelligible to researchers in the artifi-
cial intelligence and puter munities. Open research issues,
suggesting promising directions for future research, are also discussed.
L ORENZA S AITTA is Full Professor puter Science at the University
of Piemonte Orientale, Italy.
A TTILIO G IORDANA is Full Professor puter Science at the Univer-
sity of Piemonte Orientale, Italy.
A NTOINE C ORNUEJOLS´ is Full Professor puter Science at the
AgroParisTech Engineering School, Paris.
Phase Transitions
in Machine Learning
LORENZA SAITTA
University of Piemonte Orientale, Italy
ATTILIO GIORDANA
University of Piemonte Orientale, Italy
ANTOINE CORNUEJOLS´
AgroParisTech Engineering School, Paris, France
CAMBRIDGE UNIVERSITY PRESS
Cambridge, New York, Melbourne, Madrid, Cape Town,
Singapore, ˜aS o Paulo, Delhi, Tokyo, Mexico City
Cambridge University Press
The Edinburgh Building, Cambridge CB2 8RU, UK
Published in the United States of America by Cambridge University Press, New York
Information on this title: 0521763912
C L. Saitta, A. Giordana and A. Cornuejols´ 2011
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements