Kernel Adaptive Filtering. A Comprehensive Introduction

✅ Online learning from a signal 🚦 processing perspective There is increased interest in kernel learning algorithms in neural networks and ➕ a growing 💗 need for nonlinear adaptive algorithms in advanced signal 🚦 processing, communications, and ➕ controls 🎛️. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authorsWeb site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is the 🥇 📚️ to 🎁 a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert 🌌. Based on research being conducted in the Computational Neuro-👨‍🔬 Laboratory at the University of Florida ➕ in the Cognitive Systems Laboratory at McMaster University, Ontario, ​🇨🇦​, this unique resource elevates the adaptive filtering theory to a 🆕 🎚️, 🎁 a 🆕 design methodology of nonlinear adaptive filters. 📔 the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, ➕ the extended kernel recursive least squares algorithm 🎁 a powerful model-selection method 📞 maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their 💗 structure Features twelve 💻️-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authorsWeb site Concludes each chapter with a summary of the state of the 🎨 ➕ potential future directions for original research Kernel Adaptive Filtering is ideal for engineers 👨‍🔬, computer 💻️ scientists 👨‍🔬, and ➕ graduate 👨‍🎓️ students 👨‍🎓️ interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one 1️⃣ sample at a time ⏱️ and ➕ incremental optimal solutions are desirable). It is also a useful guide for those who look 👀 for nonlinear adaptive filtering methodologies to solve practical problems.

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