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Author ALT 2007 (2007 : Sendai-shi, Miyagi-ken, Japan)

Title Algorithmic learning theory : 18th international conference, ALT 2007, Sendai, Japan, October 1-4, 2007 : proceedings / Marcus Hutter, Rocco A. Servedio, Eiji Takimoto (eds.)
Published Berlin ; New York : Springer, [2007]
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Description 1 online resource (xi, 402 pages) : illustrations
Series Lecture notes in computer science, 0302-9743 ; 4754. Lecture notes in artificial intelligence
Lecture notes in computer science ; 4754
Lecture notes in computer science. Lecture notes in artificial intelligence.
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
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 the theoretical foundations of machine learning; they address topics such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector m
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Computer algorithms -- Congresses.
Machine learning -- Congresses.
Genre/Form Conference papers and proceedings.
Conference papers and proceedings.
Form Electronic book
Author Hutter, Marcus.
Servedio, Rocco A.
Takimoto, Eiji, 1964-
ISBN 9783540752257
Other Titles ALT 2007