Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis.
Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty.
Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance.
Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.
| ISBN: | 9783642443749 |
| Publication date: | 20th September 2014 |
| Author: | Christian Borgelt, María Ángeles Gil, João MC Sousa, Michel Verleysen |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 378 pages |
| Series: | Studies in Fuzziness and Soft Computing |
| Genres: |
Artificial intelligence Expert systems / knowledge-based systems Maths for computer scientists Computer modelling and simulation Probability and statistics Data mining |