Non-convex and Multi-objective Optimization in Data Mining

Non-convex and Multi-objective Optimization in Data Mining
Author :
Publisher :
Total Pages : 0
Release :
ISBN-10 : OCLC:780372094
ISBN-13 :
Rating : 4/5 ( Downloads)

Book Synopsis Non-convex and Multi-objective Optimization in Data Mining by : Ingo Mierswa

Download or read book Non-convex and Multi-objective Optimization in Data Mining written by Ingo Mierswa and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:


Non-convex and Multi-objective Optimization in Data Mining Related Books

Non-convex and Multi-objective Optimization in Data Mining
Language: en
Pages: 0
Authors: Ingo Mierswa
Categories:
Type: BOOK - Published: 2009 - Publisher:

GET EBOOK

Non-convex and Multi-objective Optimization in Data Mining
Language: en
Pages: 264
Authors: Ingo Mierswa
Categories:
Type: BOOK - Published: 2009 - Publisher:

GET EBOOK

Multi-Objective Optimization using Evolutionary Algorithms
Language: en
Pages: 540
Authors: Kalyanmoy Deb
Categories: Mathematics
Type: BOOK - Published: 2001-07-05 - Publisher: John Wiley & Sons

GET EBOOK

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of the
Adaptive Scalarization Methods in Multiobjective Optimization
Language: en
Pages: 247
Authors: Gabriele Eichfelder
Categories: Computers
Type: BOOK - Published: 2008-05-06 - Publisher: Springer Science & Business Media

GET EBOOK

This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. Readers will benefi
Machine Learning under Resource Constraints - Discovery in Physics
Language: en
Pages: 364
Authors: Katharina Morik
Categories: Science
Type: BOOK - Published: 2022-12-31 - Publisher: Walter de Gruyter GmbH & Co KG

GET EBOOK

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by c