LEADER 05137oam 2200697I 450 001 9911092855703321 005 20251117110938.0 010 $a1-04-005693-8 010 $a0-429-07612-6 010 $a1-4822-4140-4 010 $a9780429076121 024 7 $a10.1201/b17558 035 $a(CKB)2670000000560218 035 $a(EBL)1674068 035 $a(SSID)ssj0001350618 035 $a(PQKBManifestationID)11950160 035 $a(PQKBTitleCode)TC0001350618 035 $a(PQKBWorkID)11289085 035 $a(PQKB)10805246 035 $a(MiAaPQ)EBC1674068 035 $a(OCoLC)896597401 035 $a(FlBoTFG)9780429076121 035 $a(EXLCZ)992670000000560218 100 $a20180331h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegularization, optimization, kernels, and support vector machines /$fedited by Johan A.K. Suykens, KU Leuven, Belgium, Marco Signoretto, KU Leuven, Belgium, Andreas Argyriou, Ecole Centrale Paris, France 205 $a1st ed. 210 1$aBoca Raton :$cTaylor & Francis,$d[2015] 210 4$dİ2015 215 $a1 online resource (522 p.) 225 1 $aChapman and Hall/CRC Machine Learning & Pattern Recognition 300 $aA Chapman and Hall book. 311 08$a1-322-63809-8 311 08$a1-4822-4139-0 320 $aIncludes bibliographical references at the end of each chapters. 327 $aFront Cover; Contents; Preface; Contributors; Chapter 1: An Equivalence between the Lasso and Support Vector Machines; Chapter 2: Regularized Dictionary Learning; Chapter 3: Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization; Chapter 4: Nonconvex Proximal Splitting with Computational Errors; Chapter 5: Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning; Chapter 6: The Graph-Guided Group Lasso for Genome-Wide Association Studies 327 $aChapter 7: On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex FunctionsChapter 8: Detecting Ineffective Features for Nonparametric Regression; Chapter 9: Quadratic Basis Pursuit; Chapter 10: Robust Compressive Sensing; Chapter 11: Regularized Robust Portfolio Estimation; Chapter 12: The Why and How of Nonnegative Matrix Factorization; Chapter 13: Rank Constrained Optimization Problems in Computer Vision; Chapter 14: Low-Rank Tensor Denoising and Recovery via Convex Optimization; Chapter 15: Learning Sets and Subspaces; Chapter 16: Output Kernel Learning Methods 327 $aChapter 17: Kernel-Based Identification of Systems with Multiple Outputs Using Nuclear Norm RegularizationChapter 18: Kernel Methods for Image Denoising; Chapter 19: Single-Source Domain Adaptation with Target and Conditional Shift; Chapter 20: Multi-Layer Support Vector Machines; Chapter 21: Online Regression with Kernels 330 $aObtaining reliable models from given data is becoming increasingly important in a wide range of different applications fields including the prediction of energy consumption, complex networks, environmental modelling, biomedicine, bioinformatics, finance, process modelling, image and signal processing, brain-computer interfaces, and others. In data-driven modelling approaches one has witnessed considerable progress in the understanding of estimating flexible nonlinear models, learning and generalization aspects, optimization methods, and structured modelling. One area of high impact both in theory and applications is kernel methods and support vector machines. Optimization problems, learning, and representations of models are key ingredients in these methods. On the other hand, considerable progress has also been made on regularization of parametric models, including methods for compressed sensing and sparsity, where convex optimization plays an important role. At the international workshop ROKS 2013 Leuven, 1 July 8-10, 2013, researchers from diverse fields were meeting on the theory and applications of regularization, optimization, kernels, and support vector machines. At this occasion the present book has been edited as a follow-up to this event, with a variety of invited contributions from presenters and scientific committee members. It is a collection of recent progress and advanced contributions on these topics, addressing methods including.--$cProvided by publisher. 410 0$aChapman & Hall/CRC machine learning & pattern recognition series. 606 $aMathematical models$vCongresses 606 $aMathematical statistics$vCongresses 615 0$aMathematical models 615 0$aMathematical statistics 676 $a511.8 676 $a511/.8 686 $aCOM021030$aCOM037000$aTEC007000$2bisacsh 702 $aSuykens$b Johan A. K. 702 $aSignoretto$b Marco 702 $aArgyriou$b Andreas 712 12$aROKS (Workshop) 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9911092855703321 996 $aRegularization, optimization, kernels, and support vector machines$94737376 997 $aUNINA LEADER 03089nam 2200673Ia 450 001 9911145589303321 005 20260619225527.0 010 $a0-19-045267-6 010 $a1-282-05400-7 010 $a9786612054006 010 $a0-19-972829-1 035 $a(CKB)1000000000747105 035 $a(EBL)430745 035 $a(OCoLC)368260166 035 $a(SSID)ssj0000121006 035 $a(PQKBManifestationID)11142226 035 $a(PQKBTitleCode)TC0000121006 035 $a(PQKBWorkID)10092446 035 $a(PQKB)11297684 035 $a(MiAaPQ)EBC430745 035 $a(Au-PeEL)EBL430745 035 $a(CaPaEBR)ebr10288469 035 $a(CaONFJC)MIL205400 035 $a(MiAaPQ)EBC5797988 035 $a(FINmELB)ELB167222 035 $a(EXLCZ)991000000000747105 100 $a20080923d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aChildren and pollution $ewhy scientists disagree /$fColleen F. Moore 205 $a1st ed. 210 $aOxford ;$aNew York $cOxford University Press$d2009 215 $a1 online resource (375 p.) 300 $aDescription based upon print version of record. 311 08$a0-19-538666-3 320 $aIncludes bibliographical references (p. 259-338) and index. 327 $aContents; Prologue; 1 Lead and the Roots of Environmental Controversies; 2 Mercury: Not Just a Fish Story; 3 PCBs: Another Global Pollutant; 4 Neurotoxins Influence Neurodevelopment: Organophosphorus and Carbamate Pesticides; 5 Noise: A Barrier to Children's Learning; 6 It Isn't Fair: Environmental Pollution Disasters and Community Relocations; 7 The Best Science, Values, and the Precautionary Principle to Protect Children; 8 Protect Your Family, Protect Our Planet; Afterword: A Few Remarks on Global Warming; Appendix: Understanding Two Important Statistical Concepts; Endnotes; References 327 $aIndex 330 $aHow does pollution impact our daily quality of life? What are the effects of pollution on children's development? Why do industry and environmental experts disagree about what levels of pollutants are safe? In this clearly written book, Moore traces the debates around five key pollutants---lead, mercury, noise, pesticides, and dioxins and PCBs---and provides an overview of the history of each pollutant, basic research findings, and the scientific and regulatory controversies surrounding it. Moore focuses, in particular, on the impact of these pollutants on children's psychological development- 606 $aPediatric toxicology 606 $aEnvironmental toxicology 606 $aPollution$xPsychological aspects 615 0$aPediatric toxicology. 615 0$aEnvironmental toxicology. 615 0$aPollution$xPsychological aspects. 676 $a618.92 676 $a618.92/98 676 $a618.9298 676 $a618.9298 700 $aMoore$b Colleen F.$f1950- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911145589303321 997 $aUNINA