Algorithmic Learning Theory

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

Häftad, Engelska, 2002

Av Nicolò Cesa-Bianchi, Masayuki Numao, Rüdiger Reischuk, Masayuki Cesa-Bianchi, Nicolò

729 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

This book constitutes the refereed proceedings of the 13th International Conference on Algorithmic Learning Theory, ALT 2002, held in Lubeck, Germany in November 2002. The 26 revised full papers presented together with 5 invited contributions and an introduction were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on learning Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, inductive inference, inductive logic programming, language learning, statistical learning, and applications and heuristics.

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

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Programmeringsböcker inom Data och it
Artificiell intelligens inom Data och it

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.