Multi-Objective Optimization utilizing Cluster Analysis applied to Dimensional Transposed Problems - Karsten Wendt
-30% ar kodu BOOKS
Piegāde 12-18 darba dienu laikā
30 dienu atgriešanas politika
With respect to the importance of multi-objective optimization in the context of the today¿s information processing and analysis, as well as the limitation of current approaches to treat large and complex tasks in practical time and little adjustment costs, this work proposes a novel optimization concept, based on data domain transformations and subsequent cluster analyses to solve multi-objective optimizat ... Pilns apraksts
Jums varētu patikt arī
TOP
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI
Eliezer Yudkowsky, Nate Soares
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
We Are Not Machines: The Fight for the Future of Work
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Open Circuits: The Inner Beauty of Electronic Components
Eric Schlaepfer, Windell Oskay
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Get started with MicroPython on Raspberry Pi Pico: The Official Raspberry Pi Pico Guide
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Practical Electronics for Inventors
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Nucleo Boards Programming with the STM32CubeIDE: Hands-on in more than 50 projects
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
INCOSE Systems Engineering Handbook
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Fundamentals of Aerodynamics ISE
Christopher Cadou, John D. Anderson
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Black Holes: The Key to Understanding the Universe
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
KiCad Like A Pro - Fundamentals and Projects: Getting started with the world's best open-source PCB tool
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Make: Electronics: Learning by Discovery: A hands-on primer for the new electronics enthusiast
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Idea Factory: Bell Labs and the Great Age of American Innovation
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Flowers For Algernon: A Modern Literary Classic
-30% ar kodu BOOKS
Piegādātāja noliktavā
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% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Professional SMD Assembly: Practical Low-Cost Methods for Reliable PCB Production
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Blood in the Machine: The Origins of the Rebellion Against Big Tech
-30% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Mastering the Arduino Uno R4: Programming and Projects for the Minima and WiFi
-30% ar kodu BOOKS
Piegādātāja noliktavā
Aprašymas
With respect to the importance of multi-objective optimization in the context of the today¿s information processing and analysis, as well as the limitation of current approaches to treat large and complex tasks in practical time and little adjustment costs, this work proposes a novel optimization concept, based on data domain transformations and subsequent cluster analyses to solve multi-objective optimization problems. The approach abstracts the transposition of large, high-dimensional and diverse data models to low-dimensional uniform equivalents within an independent framework, which is optimized regarding data similarity conservation, i.e. the semantic relations of the data items to each other are preserved, and low runtime complexity, i.e. linearly increasing model sizes also cause only linearly growing runtimes in spite of the consideration of all data relations. The cluster analysis step is represented by an enhanced version of the k-Means algorithm, which is designed to group large numbers of data items to large numbers of clusters with also linear time complexity. Applying and adapting these both components to generic segmentation and pattern recognition tasks as two representative multi-objective optimization problems, illustrate and prove the usability of the proposed concept, by solving these tasks with high qualities of results and low runtimes with virtually linear time complexities. The abstracted components, as well as their application extensions are tested and analyzed during full factorial design tests, utilizing artificial, scalable data models, to determine valid parameter ranges, qualities of results and runtimes, as well as to ensure repeatable and comparable tests.
Vairāk informācijas
| Autors | Karsten Wendt |
|---|---|
| Izdevējs | TUDpress Verlag der Wissenschaften GmbH |
| Izlaides gads | 2016 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783959080422 |