Algorithmic Learning Theory

Marcus Hutter

ISBN 10: 3540752242 / ISBN 13: 9783540752240

Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2007, 2007

Riguardo questo articolo

This item is printed on demand - Print on Demand Titel. Neuware -This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory (ALT 2007), which was held in Sendai (Japan) during October 1¿4, 2007. The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scienti c interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference. The conference was co-located with the Tenth International Conference on Discovery Science (DS 2007). This volume includes 25 technical contributions that were selected from 50 submissions by the ProgramCommittee. It also contains descriptions of the ve invited talks of ALT and DS; longer versions of the DS papers are available in the proceedings of DS 2007. These invited talks were presented to the audience of both conferences in joint sessions. 420 pp. Englisch.

Codice articolo 9783540752240

Dati bibliografici

• Titolo: Algorithmic Learning Theory
• Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2007
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 3540752242
• ISBN 13: 9783540752240
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 23 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 23 millimetri di profondità
• Peso: 633 grammi

Descrizione articolo

Riassunto

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 International Conference on Discovery Science, DS 2007. The 25 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 50 submissions. They are dedicated to the theoretical foundations of machine learning.

Contenuti

Editors’ Introduction.- Editors’ Introduction.- Invited Papers.- A Theory of Similarity Functions for Learning and Clustering.- Machine Learning in Ecosystem Informatics.- Challenge for Info-plosion.- A Hilbert Space Embedding for Distributions.- Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity.- Invited Papers.- Feasible Iteration of Feasible Learning Functionals.- Parallelism Increases Iterative Learning Power.- Prescribed Learning of R.E. Classes.- Learning in Friedberg Numberings.- Complexity Aspects of Learning.- 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 Learning.- Online Learning.- Tuning Bandit Algorithms in Stochastic Environments.- Following the Perturbed Leader to Gamble at Multi-armed Bandits.- Online Regression Competitive with Changing Predictors.- Unsupervised Learning.- Cluster Identification in Nearest-Neighbor Graphs.- Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in .- Language Learning.- Learning Efficiency of Very Simple Grammars from Positive Data.- Learning Rational Stochastic Tree Languages.- Query Learning.- One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples.- Polynomial Time Algorithms for Learning k-Reversible Languages and Pattern Languages with Correction Queries.- Learning and Verifying Graphs Using Queries with a Focus on Edge Counting.- Exact Learning of Finite Unions of Graph Patterns from Queries.- Kernel-Based Learning.- Polynomial Summaries of Positive Semidefinite Kernels.- Learning Kernel Perceptrons on Noisy Data Using Random Projections.- Continuity of Performance Metrics for Thin Feature Maps.- Other Directions.- Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability.- Pseudometrics for State Aggregation in Average Reward Markov Decision Processes.- On Calibration Error of Randomized Forecasting Algorithms.