Statistical Learning for Systems Modeling: Statistical Learning for Systems Modeling in Reproducing Kernel Hilbert Spaces - Okba Taouali
-30% with code BOOKS
Shipping in 12-18 days
30-day return policy
The work presented in this report falls within the framework of Machine Learning where we seek to model a non-linear system and to identify online the parameters of the model considered. This model is developed in a reproducing kernel Hilbert space (RKHS). These so-called representation or black box models are linear with respect to their parameters. They have had great success in identifying nonlinear syst ... Full description
You May Also Like
TOP
INCOSE Systems Engineering Handbook
-30% with code BOOKS
In stock at our supplier
TOP
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI
Eliezer Yudkowsky, Nate Soares
-30% with code BOOKS
In stock at our supplier
TOP
The Design of Everyday Things
-30% with code BOOKS
In stock at our supplier
TOP
Open Circuits: The Inner Beauty of Electronic Components
Eric Schlaepfer, Windell Oskay
-30% with code BOOKS
In stock at our supplier
TOP
Get started with MicroPython on Raspberry Pi Pico: The Official Raspberry Pi Pico Guide
-30% with code BOOKS
In stock at our supplier
TOP
We Are Not Machines: The Fight for the Future of Work
-30% with code BOOKS
In stock at our supplier
TOP
Foundations of Flavor: The Noma Guide to Fermentation: Including Step-By-Step Information on Making and Cooking with: Koji, Kombuchas, Shoyus, Misos, Vinegars, Garums, Lacto-Ferments and black Fruits Vegetables
-30% with code BOOKS
In stock at our supplier
TOP
Nucleo Boards Programming with the STM32CubeIDE: Hands-on in more than 50 projects
-30% with code BOOKS
In stock at our supplier
TOP
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
-30% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Aerodynamics ISE
Christopher Cadou, John D. Anderson
-30% with code BOOKS
In stock at our supplier
TOP
Black Holes: The Key to Understanding the Universe
-30% with code BOOKS
In stock at our supplier
TOP
Practical Electronics for Inventors
-30% with code BOOKS
In stock at our supplier
TOP
Flowers For Algernon: A Modern Literary Classic
-30% with code BOOKS
In stock at our supplier
TOP
Human Compatible: Artificial Intelligence and the Problem of Control
-30% with code BOOKS
In stock at our supplier
TOP
How To Read Water: Clues & Patterns from Puddles to the Sea
-30% with code BOOKS
In stock at our supplier
TOP
Make: Electronics: Learning by Discovery: A hands-on primer for the new electronics enthusiast
-30% with code BOOKS
In stock at our supplier
TOP
Mastering the Arduino Uno R4: Programming and Projects for the Minima and WiFi
-30% with code BOOKS
In stock at our supplier
TOP
The Idea Factory: Bell Labs and the Great Age of American Innovation
-30% with code BOOKS
In stock at our supplier
Description
The work presented in this report falls within the framework of Machine Learning where we seek to model a non-linear system and to identify online the parameters of the model considered. This model is developed in a reproducing kernel Hilbert space (RKHS). These so-called representation or black box models are linear with respect to their parameters. They have had great success in identifying nonlinear systems using kernel methods.
More Information
| Author | Okba Taouali |
|---|---|
| Publisher | Our Knowledge Publishing |
| Release year | 2023 |
| Cover type | Softcover |
| EAN | 9786206575672 |