Produktbild: Algorithmic Learning Theory
Band 2842

Algorithmic Learning Theory 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

07.10.2003

Herausgeber

Ricard Gavaldà + weitere

Verlag

Springer Berlin

Seitenzahl

320

Maße (L/B/H)

23,6/15,7/2 cm

Gewicht

467 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-20291-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

07.10.2003

Herausgeber

Verlag

Springer Berlin

Seitenzahl

320

Maße (L/B/H)

23,6/15,7/2 cm

Gewicht

467 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-20291-2

Herstelleradresse

Springer Nature Customer Service Center GmbH
Europaplatz 3
69115 Heidelberg
DE
ProductSafety@springernature.com

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  • Produktbild: Algorithmic Learning Theory
  • Invited Papers.- Abduction and the Dualization Problem.- Signal Extraction and Knowledge Discovery Based on Statistical Modeling.- Association Computation for Information Access.- Efficient Data Representations That Preserve Information.- Can Learning in the Limit Be Done Efficiently?.- Inductive Inference.- Intrinsic Complexity of Uniform Learning.- On Ordinal VC-Dimension and Some Notions of Complexity.- Learning of Erasing Primitive Formal Systems from Positive Examples.- Changing the Inference Type – Keeping the Hypothesis Space.- Learning and Information Extraction.- Robust Inference of Relevant Attributes.- Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables.- Learning with Queries.- On the Learnability of Erasing Pattern Languages in the Query Model.- Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries.- Learning with Non-linear Optimization.- Kernel Trick Embedded Gaussian Mixture Model.- Efficiently Learning the Metric with Side-Information.- Learning Continuous Latent Variable Models with Bregman Divergences.- A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation.- Learning from Random Examples.- On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays.- Learning a Subclass of Regular Patterns in Polynomial Time.- Identification with Probability One of Stochastic Deterministic Linear Languages.- Online Prediction.- Criterion of Calibration for Transductive Confidence Machine with Limited Feedback.- Well-Calibrated Predictions from Online Compression Models.- Transductive Confidence Machine Is Universal.- On the Existence and Convergence of Computable Universal Priors.