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

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 Labelling
Multi Channel Sequence Processing
Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis
Extensions of the Informative Vector Machine
Efficient Communication by Breathing
Guiding Local Regression Using Visualisation
Transformations of Gaussian Process Priors
Kernel Based Learning Methods：Regularization Networks and RBF Networks
Redundant Bit Vectors for Quickly Searching High—Dimensional Regions
Bayesian Independent Component Analysis with Prior Constraints：An
Application in Biosignal Analysis
Ensemble Algorithms for Feature Selection
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
Understanding Gaussian Process Regression Using the Equivalent Kernel
Integrating Binding Site Predictions Using Non—linear Classification Methods
Support Vector Machine to Synthesise Kernels
Appropriate Kernel Functions for with Sequences of Symbolic Data
Support Vector Machine Learning
Variational Bayes Estimation of Mixing Coefficients
A Comparison of Condition Numbers for the Full Rank Least Squares Problem
SVM Based Learning System for Information Extraction
Author Inde