LEADER 06853nam 22008535 450 001 9910484432703321 005 20251226202137.0 010 $a3-540-69939-2 024 7 $a10.1007/978-3-540-69939-2 035 $a(CKB)1000000000440813 035 $a(SSID)ssj0000355445 035 $a(PQKBManifestationID)11294234 035 $a(PQKBTitleCode)TC0000355445 035 $a(PQKBWorkID)10337784 035 $a(PQKB)10665117 035 $a(DE-He213)978-3-540-69939-2 035 $a(MiAaPQ)EBC3068649 035 $a(PPN)127051988 035 $a(BIP)23280935 035 $a(EXLCZ)991000000000440813 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aArtificial Neural Networks in Pattern Recognition $eThird IAPR TC3 Workshop, ANNPR 2008 Paris, France, July 2-4, 2008, Proceedings /$fedited by Lionel Prevost, Simone Marinai, Friedhelm Schwenker 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (IX, 322 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5064 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-69938-4 320 $aIncludes bibliographical references and index. 327 $aUnsupervised Learning -- Patch Relational Neural Gas ? Clustering of Huge Dissimilarity Datasets -- The Block Generative Topographic Mapping -- Kernel k-Means Clustering Applied to Vector Space Embeddings of Graphs -- Probabilistic Models Based on the ?-Sigmoid Distribution -- How Robust Is a Probabilistic Neural VLSI System Against Environmental Noise -- Supervised Learning -- Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis -- Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network -- Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics -- Neural Approximation of Monte Carlo Policy Evaluation Deployed in Connect Four -- Cyclostationary Neural Networks for Air Pollutant Concentration Prediction -- Fuzzy Evolutionary Probabilistic Neural Networks -- Experiments with Supervised Fuzzy LVQ -- A Neural Network Approach to Similarity Learning -- Partial Discriminative Training of Neural Networks for Classification of Overlapping Classes -- Multiple Classifiers -- Boosting Threshold Classifiers for High? Dimensional Data in Functional Genomics -- Decision Fusion on Boosting Ensembles -- The Mixture of Neural Networks as Ensemble Combiner -- Combining Methods for Dynamic Multiple Classifier Systems -- Researching on Multi-net Systems Based on Stacked Generalization -- Applications -- Real-Time Emotion Recognition from Speech Using Echo State Networks -- Sentence Understanding and Learning of New Words with Large-Scale Neural Networks -- Multi-class Vehicle Type Recognition System -- A Bio-inspired Neural Model for Colour Image Segmentation -- Mining Software Aging Patterns by Artificial Neural Networks -- Bayesian Classifiers for Predicting the Outcome of Breast Cancer Preoperative Chemotherapy -- Feature Selection -- Feature Ranking Ensembles for Facial Action Unit Classification -- Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration -- Improving Features Subset Selection Using Genetic Algorithms for Iris Recognition -- Artificial Neural Network Based Automatic Face Model Generation System from Only One Fingerprint. 330 $aTheThirdIAPRTC3WorkshoponArti'cialNeuralNetworksinPatternRec- nition, ANNPR 2008, was held at Pierre and Marie Curie University in Paris (France), July 2-4, 2008. The workshop was organized by the Technical C- mittee on Neural Networks and Computational Intelligence (TC3) that is one of the 20 TCs of the International Association for Pattern Recognition (IAPR). The scope of TC3 includes computational intelligence approaches, such as fuzzy systems, evolutionary computing and arti'cial neural networks and their use in various pattern recognition applications. ANNPR 2008 followed the success of the previous workshops: ANNPR 2003 held at the University of Florence (Italy) andANPPR 2006held at ReisensburgCastle, Universityof Ulm (Germany).All the workshops featured a single-track program including both oral sessions and posters with a focus on active participation from every participant. Inrecentyears,the'eld ofneuralnetworkshasmaturedconsiderablyinboth methodologyandreal-worldapplications.Asre'ectedinthisbook,arti'cialn- ral networks in pattern recognition combine many ideas from machine learning, advanced statistics, signal and image processing for solving complex real-world pattern recognition problems. High quality across such a diverse ?eld of research can only be achieved through a rigorous and selective review process. For this workshop, 57 papers were submitted out of which 29 were selected for inclusion in the proceedings. The oral sessions included 18 papers, while 11 contributions were presented as posters. ANNPR 2008 featured research works in the areas of supervised and unsupervised learning, multiple classi'er systems, pattern recognition in signal and image processing, and feature selection. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5064 606 $aData mining 606 $aComputer engineering 606 $aComputer networks 606 $aPattern recognition systems 606 $aArtificial intelligence 606 $aApplication software 606 $aBiometric identification 606 $aData Mining and Knowledge Discovery 606 $aComputer Engineering and Networks 606 $aAutomated Pattern Recognition 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 606 $aBiometrics 615 0$aData mining. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aPattern recognition systems. 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aBiometric identification. 615 14$aData Mining and Knowledge Discovery. 615 24$aComputer Engineering and Networks. 615 24$aAutomated Pattern Recognition. 615 24$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 615 24$aBiometrics. 676 $a006.312 701 $aPrevost$b Lionel$01760003 701 $aMarinai$b Simone$0508419 701 $aSchwenker$b Friedhelm$01361227 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484432703321 996 $aArtificial neural networks in pattern recognition$94198723 997 $aUNINA