Stochastic Algorithms: Foundations and Applications : 5th International Symposium, SAGA 2009 Sapporo, Japan, October 26-28, 2009 Proceedings / edited by Osamu Watanabe, Thomas Zeugmann.

Format:

Book

Contributor:

Watanabe, Osamu, 1958- editor.

Zeugmann, Thomas, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 5792.

Theoretical Computer Science and General Issues ; 5792

Language:

English

Subjects (All):

Computers.

Data structures (Computer science).

Probabilities.

Algorithms.

Mathematical statistics.

Theory of Computation.

Data Structures.

Probability Theory and Stochastic Processes.

Algorithm Analysis and Problem Complexity.

Computation by Abstract Devices.

Probability and Statistics in Computer Science.

Local Subjects:

Theory of Computation.

Data Structures.

Probability Theory and Stochastic Processes.

Algorithm Analysis and Problem Complexity.

Computation by Abstract Devices.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource (X, 221 pages).

Edition:

First edition 2009.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the 5th International Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009, held in Sapporo, Japan, in October 2009. The 15 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections on learning, graphs, testing, optimization and caching, as well as stochastic algorithms in bioinformatics.

Contents:

Invited Papers

Scenario Reduction Techniques in Stochastic Programming

Statistical Learning of Probabilistic BDDs

Regular Contributions

Learning Volatility of Discrete Time Series Using Prediction with Expert Advice

Prediction of Long-Range Dependent Time Series Data with Performance Guarantee

Bipartite Graph Representation of Multiple Decision Table Classifiers

Bounds for Multistage Stochastic Programs Using Supervised Learning Strategies

On Evolvability: The Swapping Algorithm, Product Distributions, and Covariance

A Generic Algorithm for Approximately Solving Stochastic Graph Optimization Problems

How to Design a Linear Cover Time Random Walk on a Finite Graph

Propagation Connectivity of Random Hypergraphs

Graph Embedding through Random Walk for Shortest Paths Problems

Relational Properties Expressible with One Universal Quantifier Are Testable

Theoretical Analysis of Local Search in Software Testing

Firefly Algorithms for Multimodal Optimization

Economical Caching with Stochastic Prices

Markov Modelling of Mitochondrial BAK Activation Kinetics during Apoptosis

Stochastic Dynamics of Logistic Tumor Growth.

Other Format:

Printed edition:

ISBN:

978-3-642-04944-6

9783642049446

Access Restriction:

Restricted for use by site license.