Willkommen, schön sind Sie da!
Logo Ex Libris

Algorithmic Learning Theory

  • E-Book (pdf)
  • 372 Seiten
(0) Erste Bewertung abgeben
Alle Bewertungen ansehen
This book constitutes the refereed proceedings of the 10th International Conference on Algorithmic Learning Theory, ALT'99, held ... Weiterlesen
E-Books ganz einfach mit der kostenlosen Ex Libris-Reader-App lesen. Hiererhalten Sie Ihren Download-Link.
CHF 106.90
Download steht sofort bereit
Informationen zu E-Books
E-Books eignen sich auch für mobile Geräte (sehen Sie dazu die Anleitungen).
E-Books von Ex Libris sind mit Adobe DRM kopiergeschützt: Erfahren Sie mehr.
Weitere Informationen finden Sie hier.
Bestellung & Lieferung in eine Filiale möglich


This book constitutes the refereed proceedings of the 10th International Conference on Algorithmic Learning Theory, ALT'99, held in Tokyo, Japan, in December 1999.The 26 full papers presented were carefully reviewed and selected from a total of 51 submissions. Also included are three invited papers. The papers are organized in sections on Learning Dimension, Inductive Inference, Inductive Logic Programming, PAC Learning, Mathematical Tools for Learning, Learning Recursive Functions, Query Learning and On-Line Learning.


Invited Lectures.- Tailoring Representations to Different Requirements.- Theoretical Views of Boosting and Applications.- Extended Stochastic Complexity and Minimax Relative Loss Analysis.- Regular Contributions.- Algebraic Analysis for Singular Statistical Estimation.- Generalization Error of Linear Neural Networks in Unidentifiable Cases.- The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa.- The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract).- The VC-Dimension of Subclasses of Pattern Languages.- On the V ? Dimension for Regression in Reproducing Kernel Hilbert Spaces.- On the Strength of Incremental Learning.- Learning from Random Text.- Inductive Learning with Corroboration.- Flattening and Implication.- Induction of Logic Programs Based on ?-Terms.- Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any.- A Method of Similarity-Driven Knowledge Revision for Type Specializations.- PAC Learning with Nasty Noise.- Positive and Unlabeled Examples Help Learning.- Learning Real Polynomials with a Turing Machine.- Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm.- A Note on Support Vector Machine Degeneracy.- Learnability of Enumerable Classes of Recursive Functions from "Typical" Examples.- On the Uniform Learnability of Approximations to Non-recursive Functions.- Learning Minimal Covers of Functional Dependencies with Queries.- Boolean Formulas Are Hard to Learn for Most Gate Bases.- Finding Relevant Variables in PAC Model with Membership Queries.- General Linear Relations among Different Types of Predictive Complexity.- Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph.- On Learning Unions of Pattern Languages and Tree Patterns.


Titel: Algorithmic Learning Theory
Untertitel: 10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings
EAN: 9783540467694
Digitaler Kopierschutz: Wasserzeichen
Format: E-Book (pdf)
Hersteller: Springer Berlin Heidelberg
Genre: IT & Internet
Anzahl Seiten: 372
Veröffentlichung: 05.03.2007
Dateigrösse: 7.8 MB

Weitere Bände aus der Buchreihe "Lecture Notes in Artificial Intelligence"