Adaptive and Natural Computing Algorithms 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II

The two volume set LNCS 4431 and LNCS 4432 constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. The 178 revised full papers presented were carefully reviewed and selected from a tot...

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Main Authors Beliczynski, Bartlomiej, Dzielinski, Andrzej, Iwanowski, Marcin, Ribeiro, Bernadete
Format eBook
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin / Heidelberg 2007
Springer
Edition1
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN9783540715900
3540715908

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Abstract The two volume set LNCS 4431 and LNCS 4432 constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. The 178 revised full papers presented were carefully reviewed and selected from a total of 474 submissions.
AbstractList The two volume set LNCS 4431 and LNCS 4432 constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. The 178 revised full papers presented were carefully reviewed and selected from a total of 474 submissions.
Author Beliczynski, Bartlomiej
Iwanowski, Marcin
Ribeiro, Bernadete
Dzielinski, Andrzej
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Snippet The two volume set LNCS 4431 and LNCS 4432 constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing...
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Subtitle 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II
TableOfContents Support Vector Machine Detection ofPeer-to-Peer Traffic in High-PerformanceRouters with Packet Sampling -- Introduction -- Problem Modeling -- Performance Evaluation, CISCO 7206 -- Training and Testing Sets -- Parameter Options in SVM Training -- gamma Shift -- Faster Preprocessing Stage -- Performance Evaluation, Juniper M10 -- Comparison with Other Methods -- Conclusions -- Improving SVM Performance Using a LinearCombination of Kernels -- Introduction -- Related Work -- Proposed Model -- Representation -- Model -- Algorithms -- Experiments -- Test Problems -- Numerical Experiments -- Conclusions -- Boosting RVM Classifiers for Large Data Sets -- Introduction -- Relevance Vector Machines -- Boosting -- Boosting RVM -- Case Study: Text Classification -- Data Set -- Performance Measures -- Baseline Results -- RVM Text Boosting -- Conclusions and Future Work -- Multi-class Support Vector Machines Based onArranged Decision Graphs and Particle SwarmOptimization for Model Selection -- Introduction -- Building the Set of Binary Classifiers -- Model Selection -- PSO for Tuning SVM Parameters -- Graph Order -- Results and Discussion -- Conclusion -- Applying Dynamic Fuzzy Model in Combinationwith Support Vector Machine to Explore StockMarket Dynamism -- Introduction -- Methodology and Architecture -- Dynamic Fuzzy Model -- Model Optimization with Genetic Algorithm(GA) -- Support Vector Machine(SVM) -- Experiment -- Conclusions -- Predicting Mechanical Properties of RubberCompounds with Neural Networks and SupportVector Machines -- Introduction -- Neural Networks and Support Vector Machines -- Rubber Compound Database -- Preprocessing -- Experiments -- Hardness -- Density -- Conclusions -- An Evolutionary Programming Based SVM EnsembleModel for Corporate Failure Prediction -- Introduction -- Methodology Formulation Process -- Stage I: Data Sampling
Introduction -- Experimental Conditions -- Method for Improving Speech Characteristics -- The Design of the Speech Enhancement System -- Time-Delay Neural Network (TDNN) -- Experimental Results and Considerations -- The Effects of Coefficients B_f and R -- Performance Evaluation and Comparison for Speech Enhancement -- Conclusions -- References -- Recognition of Patterns Without Feature Extraction byGRNN -- Introduction -- Overview of GRNN Structure -- The Realization of the System for Pattern Recognition -- Simulation and Results -- Object Recognition -- Handwritten Digit Recognition -- Conclusion -- References -- Real-Time String Filtering of Large DatabasesImplemented Via a Combination ofArtificial Neural Networks -- Introduction -- Traditional String Filtering -- Proposed String Filtering Approach -- Approximate Matching -- Exact Matching -- Database Updating -- Demonstration of String Filtering -- Conclusions -- References -- Parallel Realizations of the SAMANN Algorithm -- Introduction -- A Neural Network for Sammon's Projection -- Strategies of the SAMANN Algorithm Parallelization -- The First Strategy of the Parallel Realization -- The Second Strategy of the Parallel Realization -- Conclusions -- A POD-Based Center Selection for RBF NeuralNetwork in Time Series Prediction Problems -- Introduction -- HCM and OLS Center Selections -- HCM Center Selection -- OLS Center Selection -- Methodology -- Numerical Experiments -- Conclusions and Discussions -- References -- Support, Relevance and Spectral Learning forTime Series -- Introduction -- Support Vector Machines -- Relevance Vector Machines -- Constructive Approach: Fast Marginal Likelihood Maximization -- Spectral Clustering Kernel Machine -- Results and Discussion -- Gas Furnace of Box Jenkins -- Mackey-Glass Chaotic Time Series -- Conclusions
Conclusion -- A Study into the Improvement of BinaryHopfield Networks for Map Coloring -- Introduction -- Network Architecture -- The Proposed Algorithm for the Map-Coloring Problem -- Simulation Results -- Conclusions -- Automatic Diagnosis of the FootprintPathologies Based on Neural Networks -- Introduction -- Footprint Representation and Characteristics Extraction -- Training of the Neural Network Classifier -- Validation of the Neural Network Classifier -- Final Remarks and Future Studies -- Mining Data from a Metallurgical Process by a NovelNeural Network Pruning Method -- Introduction -- The Method -- The Pruning Algorithm -- An Example -- Extension to On-Line Learning -- Application to Hot Metal Silicon Prediction -- The Prediction Problem and Process Data -- Results -- Conclusions -- References -- Dynamic Ridge Polynomial Neural Networks inExchange Rates Time Series Forecasting -- Introduction -- The Networks -- Ridge Polynomial Neural Network (RPNN) -- Dynamic Ridge Polynomial Neural Network (DRPNN) -- Learning Algorithm for the Proposed Network -- Financial Time Series -- Simulation Results and Discussion -- Conclusion -- References -- Neural Systems for Short-Term Forecasting ofElectric Power Load -- Introduction -- AI Systems for Short-Term Forecasting of Electric Power Load -- A Hierarchical Hybrid Forecasting System -- Single Multi-layer Neural Network -- Modular Neural System -- Committee Neural System -- Rule-Aided Neural System -- Concluding Remarks -- Jet Engine Turbine and CompressorCharacteristics Approximation by Means ofArtificial Neural Networks -- Introduction -- Turbine and Compressor Characteristics -- Turbine Characteristics -- Compressor Characteristics -- Neural and Fuzzy Approximation of Turbine and Compressor Characteristics -- Conclusion -- Speech Enhancement System Based on AuditorySystem and Time-Delay Neural Network
Stage II: Individual SVM Classifiers Creation
Intro -- Title Page -- Preface -- Organization -- Table of Contents - Part II -- Evolution of Multi-class Single Layer Perceptron -- Introduction -- Minimum Empirical Error and SV Classifiers in SLP Training -- Practical Aspects of Training the K-Class Network of SLPs -- Real World Pattern Recognition Tasks -- Concluding Remarks -- References -- Estimates of Approximation Rates byGaussian Radial-Basis Functions -- Introduction -- Approximation by Gaussian RBF Networks -- Approximation of Bessel Potentials by Gaussian RBFs -- Rates of Approximation by Bessel Potentials -- Bound on Rates of Approximation by Gaussian RBFs -- Least Mean Square vs. Outer Bounding EllipsoidAlgorithm in Confidence Estimationof the GMDH Neural Networks -- Introduction -- Synthesis of the GMDH Neural Network -- Parameters Estimation of the GMDH Neural Network -- Confidence Estimation of the Neuron Via LMS -- Confidence Estimation of the Neuron Via OBE -- An Illustrative Example -- Confidence Estimation of the Whole GMDH Network Via OBE -- Conclusions -- On Feature Extraction Capabilities of FastOrthogonal Neural Networks -- Introduction -- Classification Framework -- Normalization -- Feature Extraction -- Fast Orthogonal Neural Network Construction -- Feature Selection and Classification -- Testing Material and Simulation Results -- Testing Procedure -- Tests Results and Analysis -- Conclusion -- Neural Computations by Asymmetric Networkswith Nonlinearities -- Introduction -- Asymmetric Neural Network in the Retina -- Asymmetric Neural Network with Quadratic Nonlinearity -- Optimization of Asymmetric Neural Network -- Layered Cortex Network by Asymmetric Networks -- Conclusion -- Properties of the Hermite Activation Functionsin a Neural Approximation Scheme -- Introduction -- The Function Approximation Framework -- Hermite Functions
Properties of the Hermite Activation Functions -- Scaling Parameter and the Bandwidth -- Conclusions -- Study of the Influence of Noise in the Values of aMedian Associative Memory -- Introduction -- Basics of Median Associative Memories -- Memory Construction -- Pattern Recall -- Case of a General Fundamental Set -- Influence of Noise in Median Associative Memories -- Experiments with Real Patterns -- Recalling of the Fundamental Set of Images -- Recalling of a Pattern from a Distorted Version of it -- Conclusions -- References -- Impact of Learning on the Structural Propertiesof Neural Networks -- Introduction -- Background Theory -- Complex Systems -- Generalized Architecture of Recurrent Neural Networks (GARNN) -- Experimental Work -- Conclusion -- Learning Using a Self-building AssociativeFrequent Network -- Introduction -- The Frequent Network: Design and Construction -- The Associative Frequent Network -- Frequent Network Construction and Example -- Mining Frequent Itemsets Using F-network -- Associative Classification Based on Frequent Network -- Experimental Evaluation -- Conclusions -- Proposal of a New Conception of an Elastic NeuralNetwork and Its Application to the Solution of aTwo-Dimensional Travelling Salesman Problem -- Introduction -- Structure of the Proposed Elastic Neuron Network -- Forces Acting on a Single Neuron During the Adaptation Process -- Adaptation of an Elastic Neuron Network -- Determination of Constant Interaction Component Vectors -- Determination of Component Vectors of Elastic Interaction -- Application of a System of Two Elastic Neural Networks for the Solution of the Travelling Salesman Problem -- Other Possible Applications of the Elastic Neuron Network -- References -- Robust Stability Analysis for Delayed BAMNeural Networks -- Introduction -- Problems Statement and Preliminaries -- Main Results -- Example
Title Adaptive and Natural Computing Algorithms
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