LEADER 05038oam 2200661I 450 001 9910806147803321 005 20240402131035.0 010 $a0-429-07612-6 010 $a1-4822-4140-4 024 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(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 $a1-322-63809-8 311 $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 $a9910806147803321 996 $aRegularization, optimization, kernels, and support vector machines$94092391 997 $aUNINA