Automata, Languages and Programming自动控制、语言与编程/2005年国际会议录

2005-8-1  

Springer  

Luis,Caires  

1477  

This book constitutes the refereed proceedings of the 32nd International Colloquium on Automata, Languages and Programming, ICALP 2005, held in Lisbon, Portugal in July 2005.　　The 113 revised full papers presented together with abstracts of 5 invited talks w　ere carefully reviewed and selected from 407 submissions. The papers address all current issues in theoretical computer science and are organized in topical sections on data structures, cryptography and complexity, cryptography and distributed systems, graph algorithms, security mechanisms, automata and formal languages, signature and message authentication, algorithmic game theory, automata and logic, computational algebra, cache-oblivious algorithms and algorithmic engineering, on-line algorithms, security protocols logic, random graphs, concurrency, encryption and related primitives, approximation algorithms, games, lower bounds, probability, algebraic computation and communication complexity, string matching and computational biology, quantum complexity, analysis and verification, geometry and load balancing, concrete complexity and codes, and model theory and model checking.

Object Recognition via Local Patch LabellingMulti Channel Sequence ProcessingBayesian Kernel Learning Methods for Parametric Accelerated Life Survival AnalysisExtensions of the Informative Vector MachineEfficient Communication by BreathingGuiding Local Regression Using VisualisationTransformations of Gaussian Process PriorsKernel Based Learning Methods：Regularization Networks and RBF NetworksRedundant Bit Vectors for Quickly Searching High—Dimensional RegionsBayesian Independent Component Analysis with Prior Constraints：AnApplication in Biosignal AnalysisEnsemble Algorithms for Feature SelectionCan Gaussian Process Regression Be Made Robust Against Model Mismatch?Understanding Gaussian Process Regression Using the Equivalent KernelIntegrating Binding Site Predictions Using Non—linear Classification MethodsSupport Vector Machine to Synthesise KernelsAppropriate Kernel Functions for with Sequences of Symbolic DataSupport Vector Machine LearningVariational Bayes Estimation of Mixing CoefficientsA Comparison of Condition Numbers for the Full Rank Least Squares ProblemSVM Based Learning System for Information ExtractionAuthor Inde

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