Schölkopf bernhard (48 Ergebnisse)

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Hardback. Zustand: Good. A detailed overview of current research in kernel methods and their application to computational biology.

hardcover. Zustand: Very Good. Most of our very good books have only minor imperfections such as shelf wear consistent with a new book that's sat on a bookshop shelf for a year or two. Occasionally we may miss other minor imperfections as we have to grade books at speed. Our books are dispatched from a Yorkshire former cotton mill. We list via barcode/ISBN so please note that the images are stock images and may not be the exact copy you receive, furthermore the details about edition and year might not be accurate as many publishers reuse the same ISBN for multiple editions and as we simply scan a barcode or enter an ISBN we do not check the validity of the edition data when listing. If you're looking for an exact edition please don't order (at least not without checking with us first, although we don't always have time to check). We aim to dispatch prompty, the service used will depend on order value and book size. We can ship to most countries, see our shipping policies. Payment is via Abe only.…

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Hardcover. Zustand: Bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Jaquette abîmée. Edition 2004. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Slight signs of wear on the cover. Damaged dust jacket. Edition 2004. Ammareal gives back up to 15% of this item's net price to charity organizations.…

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Hardcover. Zustand: Brand New. 288 pages. 9.00x7.00x1.00 inches. In Stock.

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Paperback. Zustand: Brand New. 410 pages. 10.00x8.00x0.93 inches. In Stock.

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Hardcover. Zustand: Brand New. 2013 edition. 287 pages. 9.25x6.25x0.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) - more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection.These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning.- Predictive Discretization During Model Selection.- Adaptive Feature Selection in Image Segmentation.- Semi-supervised Kernel Regression Using Whitened Function Classes.- Bayesian Approaches.- Fast Monocular Bayesian Detection of Independently Moving Objects by a Moving Observer.- Kernel Density Estimation and Intrinsic Alignment for Knowledge-Driven Segmentation: Teaching Level Sets to Walk.- Vision and Faces.- 3D Head Pose Estimation with Symmetry Based Illumination Model in Low Resolution Video.- Efficient Approximations for Support Vector Machines in Object Detection.- Efficient Face Detection by a Cascaded Support Vector Machine Using Haar-Like Features.- Vision / Motion.- Differential Analysis of Two Model-Based Vehicle Tracking Approaches.- Efficient Computation of Optical Flow Using the Census Transform.- Hybrid Model-Based Estimation of Multiple Non-dominant Motions.- Biologically Motivated Approaches.- A Model of Motion, Stereo, and Monocular Depth Perception.- POI Detection Using Channel Clustering and the 2D Energy Tensor.- Segmentation.- 3D Segmentation and Quantification of Human Vessels Based on a New 3D Parametric Intensity Model.- Hierarchical Image Segmentation Based on Semidefinite Programming.- Fast Random Sample Matching of 3d Fragments.- Object Recognition.- Invariants for Discrete Structures - An Extension of Haar Integrals over Transformation Groups to Dirac Delta Functions.- Scale-Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search.- Pixel-to-Pixel Matching for Image Recognition Using Hungarian Graph Matching.- Object Recognition / Synthesis.- Estimation of Multiple Orientations at Corners and Junctions.- Phase Based Image Reconstruction in the Monogenic Scale Space.- Synthesizing Movements for Computer GameCharacters.- Poster Session.- MinOver Revisited for Incremental Support-Vector-Classification.- A Semantic Typicality Measure for Natural Scene Categorization.- Tunable Nearest Neighbor Classifier.- SVM-Based Feature Selection by Direct Objective Minimisation.- Learning with Distance Substitution Kernels.- Features for Image Retrieval: A Quantitative Comparison.- Learning from Labeled and Unlabeled Data Using Random Walks.- Learning Depth from Stereo.- Learning to Find Graph Pre-images.- Multivariate Regression via Stiefel Manifold Constraints.- Hilbertian Metrics on Probability Measures and Their Application in SVM's.- Shape from Shading Under Coplanar Light Sources.- Pose Estimation for Multi-camera Systems.- Silhouette Based Human Motion Estimation.- Cooperative Optimization for Energy Minimization in Computer Vision: A Case Study of Stereo Matching.- Building a Motion Resolution Pyramid by Combining Velocity Distributions.- A Stratified Self-Calibration Method for a Stereo Rig in Planar Motion with Varying Intrinsic Parameters.- Efficient Feature Tracking for Long Video Sequences.- Recognition of Deictic Gestures with Context.- Mosaics from Arbitrary Stereo Video Sequences.- Accurate and Efficient Approximation of the Continuous Gaussian Scale-Space.- Multi-step Entropy Based Sensor Control for Visual Object Tracking.- Spatio-temporal Segmentation Using Laserscanner and Video Sequences.- Fast Statistically Geometric Reasoning About Uncertain Line Segments in 2D- and 3D-Space.- A Statistical Measure for Evaluating Regions-of-Interest Based Attention Algorithms.- Modelling Spikes with Mixtures of Factor Analysers.- An Algorithm for Fast Pattern Recognition with Random Spikes.- The Perceptual Influence of Spatiotemporal Noise on the Reconstruction of Shape fromDynamic Occlusion.- Level Set Based Image Segmentation with Multiple Regions.- CVPIC Colour/Shape Histograms for Compressed Domain Image Retrieval.- The Redundancy Pyramid and Its Application to Segmentation on an Image Sequence.- A Higher Order MRF-Model for Stereo-Reconstruction.- Adaptive Computer Vision: Online Learning for Object Recognition.- Robust Pose Estimation for Arbitrary Objects in Complex Sc.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) - more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection.These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.…

Zustand: New. Jonas Peters is Associate Professor of Statistics at the University of Copenhagen.Dominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in T&#252bingen, Germany.Bernhard Sch&#246lkopf is Directo.

Taschenbuch. Zustand: Neu. Empirical Inference | Festschrift in Honor of Vladimir N. Vapnik | Bernhard Schölkopf (u. a.) | Taschenbuch | xix | Englisch | 2016 | Springer | EAN 9783662525111 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Zustand: Brand New. reprint edition. 308 pages. 9.30x6.20x0.73 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 422 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: New. In English.