Houman Owhadi,Clint Scovel,Gene Ryan Yoo
Kernel Mode Decomposition and the Programming of Kernels
Kernel Mode Decomposition and the Programming of Kernels
YOU SAVE £8.84
- Condition: Brand new
- UK Delivery times: Usually arrives within 2 - 3 working days
- UK Shipping: Fee starts at £2.39. Subject to product weight & dimension
Couldn't load pickup availability
Excellent- More about Kernel Mode Decomposition and the Programming of Kernels
A new approach to the classical mode decomposition problem is presented through nonlinear regression models, achieving near-machine precision in the recovery of the modes. This monograph reviews generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are related to and generalizable under the proposed framework. An alternative (programming) approach to the kernel selection problem is presented, using interpretable regression networks in additive Gaussian processes.
Format: Paperback / softback
Length: 118 pages
Publication date: 04 December 2021
Publisher: Springer Nature Switzerland AG
This monograph presents a novel approach to the classical mode decomposition problem by employing nonlinear regression models, achieving near-machine precision in mode recovery. The presentation encompasses a comprehensive review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, all of which are related to and generalizable within the proposed framework.
While kernel methods possess robust theoretical underpinnings, they necessitate the prior selection of an effective kernel. Conventional approaches to this kernel selection problem typically involve hyperparameter tuning. However, the primary objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while utilizing mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for specific tasks through the programming of interpretable regression networks within the context of additive Gaussian processes.
This approach is highly suitable for engineers, computer scientists, mathematicians, and students engaged in kernel methods, pattern recognition, and mode decomposition problems.
Weight: 209g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030821708
Edition number: 1st ed. 2021
This item can be found in:
UK and International shipping information
UK and International shipping information
UK Delivery and returns information:
- Delivery within 2 - 3 days when ordering in the UK.
- Shipping fee for UK customers from £2.39. Fully tracked shipping service available.
- Returns policy: Return within 30 days of receipt for full refund.
International deliveries:
Shulph Ink now ships to Australia, Canada, France, Ireland, Italy, Germany, Spain, Netherlands, New Zealand, United States of America, Belgium, India, United Arab Emirates.
- Delivery times: within 5 - 10 days for international orders.
- Shipping fee: charges vary for overseas orders. Only tracked services are available for international orders.
- Customs charges: If ordering to addresses outside the United Kingdom, you may or may not incur additional customs and duties fees during local delivery.
