Lecture Notes in Artificial Intelligence

Algorithmic Learning Theory

15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings

Editors: Ben David, Shai, Case, John, Maruoka, Akira (Eds.)

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About this book

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.

Table of contents (37 chapters)

  • String Pattern Discovery

    Shinohara, Ayumi

    Pages 1-13

    Preview Buy Chapter 30,19 €
  • Applications of Regularized Least Squares to Classification Problems

    Cesa-Bianchi, Nicolò

    Pages 14-18

    Preview Buy Chapter 30,19 €
  • Probabilistic Inductive Logic Programming

    Raedt, Luc (et al.)

    Pages 19-36

    Preview Buy Chapter 30,19 €
  • Hidden Markov Modelling Techniques for Haplotype Analysis

    Koivisto, Mikko (et al.)

    Pages 37-52

    Preview Buy Chapter 30,19 €
  • Learning, Logic, and Probability: A Unified View

    Domingos, Pedro

    Pages 53-53

    Preview Buy Chapter 30,19 €

Buy this book

eBook 83,29 €
price for Spain (gross)
  • ISBN 978-3-540-30215-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-540-23356-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Algorithmic Learning Theory
Book Subtitle
15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings
Editors
  • Shai Ben David
  • John Case
  • Akira Maruoka
Series Title
Lecture Notes in Artificial Intelligence
Series Volume
3244
Copyright
2004
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-30215-5
DOI
10.1007/b100989
Softcover ISBN
978-3-540-23356-5
Edition Number
1
Number of Pages
XIV, 514
Topics