Development of a modular Knowledge-Discovery Framework based on Machine Learning
Overview & Synopsis
In this work, a novel knowledge discovery framework able to analyze data produced in the Gasoline Direct Injection (GDI) context through machine learning is presented and validated. This approach is able to explore and exploit the investigated design spaces based on a limited number of observations, discovering and visualizing connections and correlations in complex phenomena. The extracted knowledge is then validated with domain expertise, revealing potential and limitations of this method.
Indexed from repository: Directory of Open Access Books
(
www.doabooks.org)
Keyboard Shortcuts:
← / → for pages • + / - for zoom • 0 to fit width
Powered by Mozilla PDF.js v3.11
Document Information
| Document Title | Development of a modular Knowledge-Discovery Framework based on Machine Learning |
|---|---|
| Author / Organization | Botticelli, Massimiliano |
| Subject & Discipline | Gasoline Direct Injection; Data-Driven Development; Machine Learning Application; Datengetriebene Entwicklung; Anwendung des Maschinellen Lernens; Knowledge Discovery; Benzin-Direkteinspritzung |
| Categories | Humanities & Social Sciences Open Access Books |
| Publication Year | 2023 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
Directory of Open Access Books
(www.doabooks.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-28531 |
| Original Source Link | Visit Source Page on www.doabooks.org » |