Stochastic Algorithms: Foundations and Applications

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.

Klappentext

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.

Inhalt
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.