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Algorithmic Learning Theory - 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Paperback, 2004 ed.)
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Algorithmic Learning Theory - 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Paperback, 2004 ed.)
Series: Lecture Notes in Artificial Intelligence, 3244
Expected to ship within 10 - 15 working days
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Algorithmic learning theory is mathematics about computer programs
which learn from experience. This involves considerable interaction
between various mathematical disciplines including theory of
computation, statistics, and c- binatorics. There is also
considerable interaction with the practical, empirical ?elds of
machine and statistical learning in which a principal aim is to
predict, from past data about phenomena, useful features of future
data from the same phenomena. The papers in this volume cover a
broad range of topics of current research in the ?eld of
algorithmic learning theory. We have divided the 29 technical,
contributed papers in this volume into eight categories
(corresponding to eight sessions) re?ecting this broad range. The
categories featured are Inductive Inf- ence, Approximate
Optimization Algorithms, Online Sequence Prediction, S- tistical
Analysis of Unlabeled Data, PAC Learning & Boosting,
Statistical - pervisedLearning, LogicBasedLearning,
andQuery&ReinforcementLearning. Below we give a brief overview
of the ?eld, placing each of these topics in the general context of
the ?eld. Formal models of automated learning re?ect various facets
of the wide range of activities that can be viewed as learning. A
?rst dichotomy is between viewing learning as an inde?nite process
and viewing it as a ?nite activity with a de?ned termination.
Inductive Inference models focus on inde?nite learning processes,
requiring only eventual success of the learner to converge to a
satisfactory conclusion.
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