LEADER 00860nam 2200289 450 001 996430551803316 005 20210805125645.0 010 $a978-88-430-5326-1 100 $a20210805d2010----km y0itay5003 ba 101 0 $aita 102 $aIT 105 $ay 00 y 200 1 $a<> vita e i buoni costumi del saggio re Carlo 5.$fChristine de Pizan$ga cura di Virginia Rossini 210 $aRoma$cCarocci$d2010 215 $a375 p.$d18 cm 225 2 $aBiblioteca medievale$v127 410 0$aBiblioteca medievale$v127 676 $a944.025 700 0$aCHRISTINE : de Pisan$0163485 702 1$aROSSINI,$bVirginia 801 0$aIT$bcba$gREICAT 912 $a996430551803316 951 $aX.1.B. 1782$b275322 L.M.$cX.1.$d548797 959 $aBK 969 $aUMA 996 $aVita e i buoni costumi del saggio re Carlo 5$91836628 997 $aUNISA LEADER 06998nam 22008415 450 001 996466062803316 005 20230726221008.0 010 $a3-540-78671-6 024 7 $a10.1007/978-3-540-78671-9 035 $a(CKB)1000000000490606 035 $a(SSID)ssj0000317916 035 $a(PQKBManifestationID)11240654 035 $a(PQKBTitleCode)TC0000317916 035 $a(PQKBWorkID)10308071 035 $a(PQKB)10974232 035 $a(DE-He213)978-3-540-78671-9 035 $a(MiAaPQ)EBC3068727 035 $a(PPN)125218516 035 $a(EXLCZ)991000000000490606 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aGenetic Programming$b[electronic resource] $e11th European Conference, EuroGP 2008, Naples, Italy, March 26-28, 2008, Proceedings /$fedited by Michael O'Neill, Leonardo Vanneschi, Steven Gustafson, Anna Isabel Esparcia Alcázar, Ivanoe De Falco, Antonio Della Cioppa, Ernesto Tarantino 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (XI, 375 p.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v4971 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-78670-8 320 $aIncludes bibliographical references and index. 327 $aOral Presentations -- Training Time and Team Composition Robustness in Evolved Multi-agent Systems -- Winning Ant Wars: Evolving a Human-Competitive Game Strategy Using Fitnessless Selection -- In Silicon No One Can Hear You Scream: Evolving Fighting Creatures -- Real-Time, Non-intrusive Speech Quality Estimation: A Signal-Based Model -- Good News: Using News Feeds with Genetic Programming to Predict Stock Prices -- A Genetic Programming Approach to Deriving the Spectral Sensitivity of an Optical System -- A SIMD Interpreter for Genetic Programming on GPU Graphics Cards -- Partitioned Incremental Evolution of Hardware Using Genetic Programming -- Population Parallel GP on the G80 GPU -- Operator Equalisation and Bloat Free GP -- Practical Model of Genetic Programming?s Performance on Rational Symbolic Regression Problems -- Semantic Building Blocks in Genetic Programming -- A Simple Powerful Constraint for Genetic Programming -- Crossover, Sampling, Bloat and the Harmful Effects of Size Limits -- The Performance of a Selection Architecture for Genetic Programming -- A Comparison of Cartesian Genetic Programming and Linear Genetic Programming -- Evolvability Via Modularity-Induced Mutational Focussing -- A Linear Estimation-of-Distribution GP System -- Feature Discovery in Reinforcement Learning Using Genetic Programming -- Hardware Accelerators for Cartesian Genetic Programming -- Genetic Programming and Class-Wise Orthogonal Transformation for Dimension Reduction in Classification Problems -- Posters -- Evolving Proactive Aggregation Protocols -- GP Classification under Imbalanced Data sets: Active Sub-sampling and AUC Approximation -- Exposing a Bias Toward Short-Length Numbers in Grammatical Evolution -- Cooperative Problem Decomposition in Pareto Competitive Classifier Models of Coevolution -- Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction -- The Effects of Constant Neutrality on Performance and Problem Hardness in GP -- Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering -- PlasmidPL: A Plasmid-Inspired Language for Genetic Programming -- Using Genetic Programming for Turing Machine Induction -- Altering Search Rates of the Meta and Solution Grammars in the mGGA. 330 $aThe 11th European Conference on Genetic Programming, EuroGP 2008, took place in Naples, Italy from 26 to 28 March in the University of Naples Congress Centre with spectacular views over the Gulf of Naples. This volume contains the papers for the 21 oral presentations and 10 posters that were presented during this time. A diverse array of topics were covered re?ecting the current state of research in the ?eld of Genetic Programming, including the latest work on representations, theory, operators and analysis, evolvable hardware, agents and numerous applications. A rigorous, double-blind peer review process was employed, with each s- mission reviewed by at least three members of the international Program C- mittee. In total 61 papers were submitted this year, making an acceptance rate of 34% for full papers, and an overall acceptance rate of 51% including posters. S- mission of papers and the reviewing process were greatly assisted by the use of the MyReview management software originally developed by Philippe Rigaux, Bertrand Chardon and other colleagues from the Universit´e Paris-Sud Orsay, France. We are especially grateful to Marc Schoenauer from INRIA, France for managing this system. Reviewers were asked to nominate keywords specifying their area of expertise, and these keywords were matched to those selected by the authors of the submitted papers with the assistance of the optimal assignment feature of the conference management software. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v4971 606 $aSoftware engineering 606 $aComputer programming 606 $aComputer science 606 $aAlgorithms 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aSoftware Engineering 606 $aProgramming Techniques 606 $aTheory of Computation 606 $aAlgorithms 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 615 0$aSoftware engineering. 615 0$aComputer programming. 615 0$aComputer science. 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 14$aSoftware Engineering. 615 24$aProgramming Techniques. 615 24$aTheory of Computation. 615 24$aAlgorithms. 615 24$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 676 $a006.3/1 702 $aO'Neill$b Michael$f1975-$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVanneschi$b Leonardo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGustafson$b Steven$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEsparcia Alcázar$b Anna Isabel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDe Falco$b Ivanoe$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDella Cioppa$b Antonio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTarantino$b Ernesto$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aEuroGP 2008 906 $aBOOK 912 $a996466062803316 996 $aGenetic Programming$9772374 997 $aUNISA LEADER 06487nam 22008535 450 001 9910143903403321 005 20200704151332.0 010 $a3-540-47917-1 024 7 $a10.1007/3-540-47917-1 035 $a(CKB)1000000000211760 035 $a(SSID)ssj0000316511 035 $a(PQKBManifestationID)11224859 035 $a(PQKBTitleCode)TC0000316511 035 $a(PQKBWorkID)10276120 035 $a(PQKB)11574192 035 $a(DE-He213)978-3-540-47917-8 035 $a(MiAaPQ)EBC3062513 035 $a(MiAaPQ)EBC6283588 035 $a(PPN)123720435 035 $a(EXLCZ)991000000000211760 100 $a20100301d2002 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aBiometric Authentication $eInternational ECCV 2002 Workshop Copenhagen, Denmark, June 1, 2002 Proceedings /$fedited by Massimo Tistarelli, Josef Bigun, Anil K. Jain 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (X, 202 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v2359 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-43723-1 320 $aIncludes bibliographical references and index. 327 $aFace Recognition I -- An Incremental Learning Algorithm for Face Recognition -- Face Recognition Based on ICA Combined with FLD -- Understanding Iconic Image-Based Face Biometrics -- Fusion of LDA and PCA for Face Verification -- Fingerprint Recognition -- Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment -- Fingerprint Matching Using Feature Space Correlation -- Fingerprint Minutiae: A Constructive Definition -- Psychology and Biometrics -- Pseudo-entropy Similarity for Human Biometrics -- Mental Characteristics of Person as Basic Biometrics -- Face Detection and Localization -- Detection of Frontal Faces in Video Streams -- Genetic Model Optimization for Hausdorff Distance-Based Face Localization -- Coarse to Fine Face Detection Based on Skin Color Adaption -- Face Recognition II -- Robust Face Recognition Using Dynamic Space Warping -- Subspace Classification for Face Recognition -- Gait and Signature Analysis -- Gait Appearance for Recognition -- View-invariant Estimation of Height and Stride for Gait Recognition -- Improvement of On-line Signature Verification System Robust to Intersession Variability -- Classifiers for Recognition -- Biometric Identification in Forensic Cases According to the Bayesian Approach -- A New Quadratic Classifier Applied to Biometric Recognition. 330 $aBiometric authentication refers to identifying an individual based on his or her distinguishing physiological and/or behavioral characteristics. It associates an individual with a previously determined identity based on that individual s appearance or behavior. Because many physiological or behavioral characteristics (biometric indicators) are distinctive to each person, biometric identifiers are inherently more reliable and more capable than knowledge-based (e.g., password) and token-based (e.g., a key) techniques in differentiating between an authorized person and a fraudulent impostor. For this reason, more and more organizations are looking to automated identity authentication systems to improve customer satisfaction, security, and operating efficiency as well as to save critical resources. Biometric authentication is a challenging pattern recognition problem; it involves more than just template matching. The intrinsic nature of biometric data must be carefully studied, analyzed, and its properties taken into account in developing suitable representation and matching algorithms. The intrinsic variability of data with time and environmental conditions, the social acceptability and invasiveness of acquisition devices, and the facility with which the data can be counterfeited must be considered in the choice of a biometric indicator for a given application. In order to deploy a biometric authentication system, one must consider its reliability, accuracy, applicability, and efficiency. 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