Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource

Edition: First edition.

ISBN: 9781118705834 , 1118705831 , 9781118705827 , 1118705823 , 9781118705810 , 1118705815 , 9781118705841 , 111870584X

Content: Offering example applications and detailed benchmarking experiments with real and synthetic datasets throughout, this book provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. --

Note: From signal processing to machine learning -- Introduction to digital signal processing -- Signal processing models -- Kernel functions and reproducing kernel hilbert spaces -- A SVM signal estimation framework -- Reproducing kernel hilbert space models for signal processing -- Dual signal models for signal processing -- Advances in kernel regression and function approximation -- Adaptive kernel learning for signal processing -- SVM and kernel classification algorithms -- Clustering and anomaly detection with kernels -- Kernel feature extraction in signal processing.

Additional Edition: Print version: Rojo-Álvarez, José Luis, 1972- Digital signal processing with kernel methods. Hoboken, NJ : John Wiley & Sons, 2017 ISBN 9781118611791

Language: English

Keywords: Electronic books. ; Electronic books. ; Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118705810

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118705810

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118705810

UID:

Format: 1 Online-Ressource (xiv, 415 Seiten)

ISBN: 9781599040448 , 1599040441

Series Statement: Premier reference source

Content: "This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing" - Provided by publisher

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-59904-042-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 1-59904-042-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-59904-043-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 1-59904-043-3

Language: English

Keywords: Bildverarbeitung ; Biologie ; Aufsatzsammlung

DOI: 10.4018/978-1-59904-042-4

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: xxvi, 672 Seiten , Illustrationen, Diagramme

ISBN: 9781118611791 , 1118611799

Additional Edition: Erscheint auch als Online-Ausgabe, PDF ISBN 9781118705827

Additional Edition: Erscheint auch als Online-Ausgabe, EPUB ISBN 9781118705834

Language: English

Keywords: Digitale Signalverarbeitung ; Kernel-based Virtual Machine

URL: http://www.wiley-vch.de/publish/dt/books/ISBN978-1-118-61179-1/

UID:

Format: 1 Online-Ressource (xxvi, 639 Seiten)

ISBN: 9781118705827 , 9781118705810

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Online-Ausgabe, epub ISBN 978-1-118-70583-4

Additional Edition: Erscheint auch als Druck-Ausgabe, cloth ISBN 978-1-118-61179-1

Language: English

Keywords: Digitale Signalverarbeitung ; Kernel-based Virtual Machine

DOI: 10.1002/9781118705810

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (668 pages) : , illustrations

ISBN: 9781118705827 (e-book)

Additional Edition: Print version: Digital signal processing with kernel methods. Hoboken, New Jersey ; Chichester, West Sussex, England : Wiley ; IEEE Press, c2018 ISBN 9781118611791

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 online resource (349 pages)

ISBN: 1-5231-4582-X , 1-63081-776-7

Language: English

UID:

Format: 1 online resource (349 pages)

ISBN: 9781630817763

Note: Machine Learning Applications in Electromagnetics and Antenna Array Processing -- Contents -- Preface -- Acknowledgments -- 1 Linear Support Vector Machines -- 1.1 Introduction -- 1.2 Learning Machines -- 1.2.1 The Structure of a Learning Machine -- 1.2.2 Learning Criteria -- 1.2.3 Algorithms -- 1.2.4 Example -- 1.2.5 Dual Representations and Dual Solutions -- 1.3 Empirical Risk and Structural Risk -- 1.4 Support Vector Machines for Classification -- 1.4.1 The SVC Criterion -- 1.4.2 Support Vector Machine Optimization -- 1.5 Support Vector Machines for Regression -- 1.5.1 The ν Support Vector Regression -- References -- 2 Linear Gaussian Processes -- 2.1 Introduction -- 2.2 The Bayes' Rule -- 2.2.1 Computing the Probability of an Event Conditional to Another -- 2.2.2 Definition of Conditional Probabilities -- 2.2.3 The Bayes' Rule and the Marginalization Operation -- 2.2.4 Independency and Conditional Independency -- 2.3 Bayesian Inference in a Linear Estimator -- 2.4 Linear Regression with Gaussian Processes -- 2.4.1 Parameter Posterior -- 2.5 Predictive Posterior Derivation -- 2.6 Dual Representation of the Predictive Posterior -- 2.6.1 Derivation of the Dual Solution -- 2.6.2 Interpretation of the Variance Term -- 2.7 Inference over the Likelihood Parameter -- 2.8 Multitask Gaussian Processes -- References -- 3 Kernels for Signal and Array Processing -- 3.1 Introduction -- 3.2 Kernel Fundamentals and Theory -- 3.2.1 Motivation for RKHS -- 3.2.2 The Kernel Trick -- 3.2.3 Some Dot Product Properties -- 3.2.4 Their Use for Kernel Construction -- 3.2.5 Kernel Eigenanalysis -- 3.2.6 Complex RKHS and Complex Kernels -- 3.3 Kernel Machine Learning -- 3.3.1 Kernel Machines and Regularization -- 3.3.2 The Importance of the Bias Kernel -- 3.3.3 Kernel Support Vector Machines -- 3.3.4 Kernel Gaussian Processes. , 3.4 Kernel Framework for Estimating Signal Models -- 3.4.1 Primal Signal Models -- 3.4.2 RKHS Signal Models -- 3.4.3 Dual Signal Models -- References -- 4 The Basic Concepts of Deep Learning -- 4.1 Introduction -- 4.2 Feedforward Neural Networks -- 4.2.1 Structure of a Feedforward Neural Network -- 4.2.2 Training Criteria and Activation Functions -- 4.2.3 ReLU for Hidden Units -- 4.2.4 Training with the BP Algorithm -- 4.3 Manifold Learning and Embedding Spaces -- 4.3.1 Manifolds, Embeddings, and Algorithms -- 4.3.2 Autoencoders -- 4.3.3 Deep Belief Networks -- References -- 5 Deep Learning Structures -- 5.1 Introduction -- 5.2 Stacked Autoencoders -- 5.3 Convolutional Neural Networks -- 5.4 Recurrent Neural Networks -- 5.4.1 Basic Recurrent Neural Network -- 5.4.2 Training a Recurrent Neural Network -- 5.4.3 Long Short-Term Memory Network -- 5.5 Variational Autoencoders -- References -- 6 Direction of Arrival Estimation -- 6.1 Introduction -- 6.2 Fundamentals of DOA Estimation -- 6.3 Conventional DOA Estimation -- 6.3.1 Subspace Methods -- 6.3.2 Rotational Invariance Technique -- 6.4 Statistical Learning Methods -- 6.4.1 Steering Field Sampling -- 6.4.2 Support Vector Machine MuSiC -- 6.5 Neural Networks for Direction of Arrival -- 6.5.1 Feature Extraction -- 6.5.2 Backpropagation Neural Network -- 6.5.3 Forward-Propagation Neural Network -- 6.5.4 Autoencoder Framework for DOA Estimation with Array Imperfections -- 6.5.5 Deep Learning for DOA Estimation with Random Arrays -- References -- 7 Beamforming -- 7.1 Introduction -- 7.2 Fundamentals of Beamforming -- 7.2.1 Analog Beamforming -- 7.2.2 Digital Beamforming/Precoding -- 7.2.3 Hybrid Beamforming -- 7.3 Conventional Beamforming -- 7.3.1 Beamforming with Spatial Reference -- 7.3.2 Beamforming with Temporal Reference -- 7.4 Support Vector Machine Beamformer -- 7.5 Beamforming with Kernels. , 7.5.1 Kernel Array Processors with Temporal Reference -- 7.5.2 Kernel Array Processor with Spatial Reference -- 7.6 RBF NN Beamformer -- 7.7 Hybrid Beamforming with Q-Learning -- References -- 8 Computational Electromagnetics -- 8.1 Introduction -- 8.2 Finite-Difference Time Domain -- 8.2.1 Deep Learning Approach -- 8.3 Finite-Difference Frequency Domain -- 8.3.1 Deep Learning Approach -- 8.4 Finite Element Method -- 8.4.1 Deep Learning Approach -- 8.5 Inverse Scattering -- 8.5.1 Nonlinear Electromagnetic Inverse Scattering Using DeepNIS -- References -- 9 Reconfigurable Antennas and Cognitive Radio -- 9.1 Introduction -- 9.2 Basic Cognitive Radio Architecture -- 9.3 Reconfiguration Mechanisms in Reconfigurable Antennas -- 9.4 Examples -- 9.4.1 Reconfigurable Fractal Antennas -- 9.4.2 Pattern Reconfigurable Microstrip Antenna -- 9.4.3 Star Reconfigurable Antenna -- 9.4.4 Reconfigurable Wideband Antenna -- 9.4.5 Frequency Reconfigurable Antenna -- 9.5 Machine Learning Implementation on Hardware -- 9.6 Conclusion -- References -- About the Authors -- Index.

Additional Edition: Print version: Martínez-Ramón, Manel Machine Learning Applications in Electromagnetics and Antenna Array Processing Norwood : Artech House,c2021 ISBN 9781630817756

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 online resource

Edition: First edition.

ISBN: 9781118705834 , 1118705831 , 9781118705827 , 1118705823 , 9781118705810 , 1118705815

Series Statement: Wiley - IEEE

Content: Offering example applications and detailed benchmarking experiments with real and synthetic datasets throughout, this book provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. --

Note: From signal processing to machine learning -- Introduction to digital signal processing -- Signal processing models -- Kernel functions and reproducing kernel hilbert spaces -- A SVM signal estimation framework -- Reproducing kernel hilbert space models for signal processing -- Dual signal models for signal processing -- Advances in kernel regression and function approximation -- Adaptive kernel learning for signal processing -- SVM and kernel classification algorithms -- Clustering and anomaly detection with kernels -- Kernel feature extraction in signal processing.

Additional Edition: Print version: Rojo-Álvarez, José Luis, 1972- Digital signal processing with kernel methods. Hoboken, NJ : John Wiley & Sons, 2017 ISBN 9781118611791

Language: English

Keywords: Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118705810