Logo - springer
Slogan - springer

Computer Science - Artificial Intelligence | Algorithmic Learning Theory - 18th International Conference, ALT 2007, Sendai, Japan, October

Algorithmic Learning Theory

18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings

Hutter, Marcus, Servedio, Rocco A., Takimoto, Eiji (Eds.)

2007, XI, 406 p.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$89.99

(net) price for USA

ISBN 978-3-540-75225-7

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$119.00

(net) price for USA

ISBN 978-3-540-75224-0

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

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. 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.

Content Level » Research

Keywords » Boosting - Support Vector Machine - algorithmic learning theory - algorithms - complexity - kernel method - learning - learning theory - machine learning - reinforcement learning - supervised learning - unsupervised learning

Related subjects » Artificial Intelligence - Database Management & Information Retrieval

Table of contents 

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.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Artificial Intelligence (incl. Robotics).