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Algorithmic learning theory : 18th international conference, ALT 2007, Sendai, Japan, October 1-4, 2007 : proceedings

Author: Marcus Hutter; Rocco A Servedio; Eiji Takimoto
Publisher: Berlin ; New York : Springer, ©2007.
Series: Lecture notes in computer science, 4754.; Lecture notes in computer science., Lecture notes in artificial intelligence.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Summary:
This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, colocated with the 10th International Conference on Discovery Science, DS 2007. The 25 revised full papers presented together with the abstracts of 5 invited papers were carefully reviewed and selected from 50 submissions. The papers are dedicated to  Read more...
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Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Additional Physical Format: Print version:
ALT 2007 (2007 : Sendai-shi, Miyagi-ken, Japan).
Algorithmic learning theory.
Berlin ; New York : Springer, ©2007
(DLC) 2007935310
(OCoLC)173602670
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Marcus Hutter; Rocco A Servedio; Eiji Takimoto
ISBN: 9783540752257 3540752250
OCLC Number: 191470200
Description: 1 online resource (xi, 402 pages) : illustrations.
Contents: Front Matter; Editors' Introduction; Machine Learning in Ecosystem Informatics; A Hilbert Space Embedding for Distributions; Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity; Feasible Iteration of Feasible Learning Functionals; Parallelism Increases Iterative Learning Power; Prescribed Learning of R.E. Classes; Learning in Friedberg Numberings; Separating Models of Learning with Faulty Teachers; Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations; Parameterized Learnability of k -Juntas and Related Problems. On Universal Transfer LearningTuning Bandit Algorithms in Stochastic Environments; Following the Perturbed Leader to Gamble at Multi-armed Bandits; Online Regression Competitive with Changing Predictors; Cluster Identification in Nearest-Neighbor Graphs; Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in \mathbb Rd; Learning Efficiency of Very Simple Grammars from Positive Data; Learning Rational Stochastic Tree Languages; One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples. Polynomial Time Algorithms for Learning k -Reversible Languages and Pattern Languages with Correction QueriesLearning and Verifying Graphs Using Queries with a Focus on Edge Counting; Exact Learning of Finite Unions of Graph Patterns from Queries; Polynomial Summaries of Positive Semidefinite Kernels; Learning Kernel Perceptrons on Noisy Data Using Random Projections; Continuity of Performance Metrics for Thin Feature Maps; Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability; Pseudometrics for State Aggregation in Average Reward Markov Decision Processes.
Series Title: Lecture notes in computer science, 4754.; Lecture notes in computer science., Lecture notes in artificial intelligence.
Other Titles: ALT 2007
Responsibility: Marcus Hutter, Rocco A. Servedio, Eiji Takimoto (eds.).

Abstract:

This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, co-located with the 10th  Read more...

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