| 书目名称 | Synergies of Soft Computing and Statistics for Intelligent Data Analysis |
| 编辑 | Rudolf Kruse,Michael R. Berthold,Olgierd Hryniewic |
| 视频video | |
| 概述 | Presents the results of the International Conference Series on Soft Methods in Probability and Statistics SMPS‘2012 held in Konstanz, Germany, October 4-6,2012.Presents recent results illustrating new |
| 丛书名称 | Advances in Intelligent Systems and Computing |
| 图书封面 |  |
| 描述 | .In recent years there has been a growing interest to extend classical methods for data analysis..The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance..Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled..About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS)..This book gathers contributions presented at the SMPS‘2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis..It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics..Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain unde |
| 出版日期 | Conference proceedings 2013 |
| 关键词 | Computational Intelligence; Intelligent Data Analysis; Soft Computing |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-33042-1 |
| isbn_softcover | 978-3-642-33041-4 |
| isbn_ebook | 978-3-642-33042-1Series ISSN 2194-5357 Series E-ISSN 2194-5365 |
| issn_series | 2194-5357 |
| copyright | Springer-Verlag Berlin Heidelberg 2013 |
发表于 2025-3-21 18:38:28
