Del i serien Lecture Notes in Computer Science

Algorithmic Learning Theory

13th International Conference, ALT 2002, Lübeck, Germany, November 24-26, 2002, Proceedings

Häftad, Engelska, 2002

AvNicolò Cesa-Bianchi,Masayuki Numao,Rüdiger Reischuk

739 kr

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This volume contains the papers presented at the 13th Annual Conference on Algorithmic Learning Theory (ALT 2002), which was held in Lub ¨ eck (Germany) during November 24–26, 2002. The main objective of the conference was to p- vide an interdisciplinary forum discussing the theoretical foundations of machine learning as well as their relevance to practical applications. The conference was colocated with the Fifth International Conference on Discovery Science (DS 2002). The volume includes 26 technical contributions which were selected by the program committee from 49 submissions. It also contains the ALT 2002 invited talks presented by Susumu Hayashi (Kobe University, Japan) on “Mathematics Based on Learning”, by John Shawe-Taylor (Royal Holloway University of L- don, UK) on “On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum”, and by Ian H. Witten (University of Waikato, New Zealand) on “Learning Structure from Sequences, with Applications in a Digital Library” (joint invited talk with DS 2002). Furthermore, this volume - cludes abstracts of the invited talks for DS 2002 presented by Gerhard Widmer (Austrian Research Institute for Arti?cial Intelligence, Vienna) on “In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project” and by Rudolf Kruse (University of Magdeburg, Germany) on “Data Mining with Graphical Models”. The complete versions of these papers are published in the DS 2002 proceedings (Lecture Notes in Arti?cial Intelligence, Vol. 2534). ALT has been awarding the E.

Produktinformation

Utgivningsdatum2002-11-13
Mått155 x 235 x 24 mm
Vikt651 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor420
Upplaga2002
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540001706

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Editors’ Introduction.- Editors’ Introduction.- Invited Papers.- Mathematics Based on Learning.- Data Mining with Graphical Models.- On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum.- In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project.- Learning Structure from Sequences, with Applications in a Digital Library.- Regular Contributions.- On Learning Monotone Boolean Functions under the Uniform Distribution.- On Learning Embedded Midbit Functions.- Maximizing Agreements and CoAgnostic Learning.- Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning.- Large Margin Classification for Moving Targets.- On the Smallest Possible Dimension and the Largest Possible Margin of Linear Arrangements Representing Given Concept Classes Uniform Distribution.- A General Dimension for Approximately Learning Boolean Functions.- The Complexity of Learning Concept Classes with Polynomial General Dimension.- On the Absence of Predictive Complexity for Some Games.- Consistency Queries in Information Extraction.- Ordered Term Tree Languages which Are Polynomial Time Inductively Inferable from Positive Data.- Reflective Inductive Inference of Recursive Functions.- Classes with Easily Learnable Subclasses.- On the Learnability of Vector Spaces.- Learning, Logic, and Topology in a Common Framework.- A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning.- Minimised Residue Hypotheses in Relevant Logic.- Compactness and Learning of Classes of Unions of Erasing Regular Pattern Languages.- A Negative Result on Inductive Inference of Extended Pattern Languages.- RBF Neural Networks and Descartes’ Rule of Signs.- Asymptotic Optimality of Transductive Confidence Machine.- An Efficient PAC Algorithm forReconstructing a Mixture of Lines.- Constraint Classification: A New Approach to Multiclass Classification.- How to Achieve Minimax Expected Kullback-Leibler Distance from an Unknown Finite Distribution.- Classification with Intersecting Rules.- Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Control.