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Algorithmic Learning Theory - Marcus Hutter#Rocco A. Servedio#Eiji Takimoto
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Marcus Hutter#Rocco A. Servedio#Eiji Takimoto:

Algorithmic Learning Theory - Paperback

2007, ISBN: 9783540752240

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, 20… More...

Nr. 14972274. Shipping costs:, Sofort lieferbar, DE. (EUR 0.00)
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Algorithmic Learning Theory - Hutter, Marcus Servedio, Rocco A. Takimoto, Eiji
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Hutter, Marcus Servedio, Rocco A. Takimoto, Eiji:

Algorithmic Learning Theory - Paperback

2007, ISBN: 9783540752240

[ED: Kartoniert / Broschiert], [PU: Springer Berlin Heidelberg], Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book constitutes the… More...

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Hutter, Marcus:
Algorithmic Learning Theory: 18th International Conference, Alt 2007, Sendai, Japan, October 1-4, 2007, Proceedings - Paperback

2007

ISBN: 9783540752240

PF, Neubuch, BRAND NEW BOOK! Shipped within 24-48 hours. Normal delivery time is 5-12 days. Please note some orders may be shipped from UK with same delivery timeframe, ***NO EXPEDITED OR… More...

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Hutter, Marcus:
Algorithmic Learning Theory: 18th International Conference, Alt 2007, Sendai, Japan, October 1-4, 2007, Proceedings - Paperback

2007, ISBN: 3540752242

[EAN: 9783540752240], Neubuch, [PU: Springer 2007-09], Books

NEW BOOK. Shipping costs: EUR 3.69 Chiron Media, Wallingford, United Kingdom [55661942] [Rating: 4 (von 5)]
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Hutter, Marcus (Editor), and Servedio, Rocco A (Editor), and Takimoto, Eiji (Editor):
Algorithmic Learning Theory - Paperback

2007, ISBN: 9783540752240

PAPERBACK, Neubuch, P 422., [PU: Springer]

Shipping costs:plus shipping costs Hawthorne, CA, Media Smart

Details of the book
Algorithmic Learning Theory

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 machines, kernel methods, complexity and learning, reinforcement learning, unsupervised learning and grammatical inference.

Details of the book - Algorithmic Learning Theory


EAN (ISBN-13): 9783540752240
ISBN (ISBN-10): 3540752242
Hardcover
Paperback
Publishing year: 2007
Publisher: Springer Berlin
402 Pages
Weight: 0,646 kg
Language: eng/Englisch

Book in our database since 2007-03-31T12:06:37-04:00 (New York)
Detail page last modified on 2022-01-15T09:38:32-05:00 (New York)
ISBN/EAN: 9783540752240

ISBN - alternate spelling:
3-540-75224-2, 978-3-540-75224-0


Information from Publisher

Author: Marcus Hutter; Rocco A. Servedio; Eiji Takimoto
Title: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Algorithmic Learning Theory - 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings
Publisher: Springer; Springer Berlin
406 Pages
Publishing year: 2007-09-17
Berlin; Heidelberg; DE
Printed / Made in
Weight: 0,640 kg
Language: English
93,08 € (DE)
95,69 € (AT)
116,11 CHF (CH)
POD

BC; Book; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Informatik; Boosting; Support Vector Machine; algorithmic learning theory; algorithms; complexity; kernel method; learning; learning theory; machine learning; reinforcement learning; supervised learning; unsupervised learning; C; Artificial Intelligence; Data Mining and Knowledge Discovery; Artificial Intelligence; Data Mining and Knowledge Discovery; Computer Science; Data Mining; Wissensbasierte Systeme, Expertensysteme; EA

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.

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