A Midsummer Night's Dream, one of the greatest stories ever told . . . in texts?!
Entertaining and easy to read, A Lawn Chair Gardener's Guide shows readers why it's time for a "Yard 2.0" and how create to create beautiful, functional, and sustainable yards that provide maximum yields with minimal effort. Pape's low-maintenance "lawn chair" gardens create habitat, conserve water, reduce the carbon footprints, and eliminate the need for chemicals.
Mixing one part gardening, one part concern for the environment, and one part personal growth, Pape sets out to change the world, one yard at a time. Best of all, she makes it easy. Pape has a knack for explaining complex topics like organic gardening and companion planting in an approachable way. You too can grow your own food, improve your little corner of the world, and transform your yard into an oasis.
<b>Online learning from a signal processing perspective</b> <p> 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. <i>Kernel Adaptive Filtering</i> 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. <ul> <li> <p> 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 <li> <p> Presents a powerful model-selection method called maximum marginal likelihood <li> <p> Addresses the principal bottleneck of kernel adaptive filters—their growing structure <li> <p> Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site <li> <p> Concludes each chapter with a summary of the state of the art and potential future directions for original research </ul> <p> <i>Kernel Adaptive Filtering</i> is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one 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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