Algorithmic Learning Theory : 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings / edited by Marcus Hutter, Rocco A. Servedio, Eiji Takimoto.

Format:

Book

Contributor:

Hutter, Marcus, editor.

Servedio, Rocco A., editor.

Takimoto, Eiji, 1964- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 4754.

Lecture Notes in Artificial Intelligence ; 4754

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XI, 406 pages).

Edition:

First edition 2007.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.

System Details:

text file PDF

Summary:

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.

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

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.

Other Format:

Printed edition:

ISBN:

978-3-540-75225-7

9783540752257

Access Restriction:

Restricted for use by site license.