Towards Advanced Data Analysis by Combining Soft Computing and Statistics

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 t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Borgelt, Christian (Editor ), Gil, María Ángeles (Editor ), Sousa, João M.C (Editor ), Verleysen, Michel (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Studies in Fuzziness and Soft Computing, 285
Temas:
Computational intelligence.
Computer science-Mathematics.
Mathematical statistics.
Data mining.
Computer simulation.
Computational Intelligence.
Probability and Statistics in Computer Science.
Data Mining and Knowledge Discovery.
Computer Modelling.
Acceso en línea:Texto Completo
Descripción
Sumario: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.
Descripción Física:X, 378 p. online resource.
ISBN:9783642302787
ISSN:1860-0808 ;

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