Band 285
Towards Advanced Data Analysis by Combining Soft Computing and Statistics
Aus der Reihe
Studies in Fuzziness and Soft Computing
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
145,99 €
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
20.09.2014
Herausgeber
Christian Borgelt + weitereVerlag
Springer BerlinSeitenzahl
378
Maße (L/B/H)
23,5/15,5/2,1 cm
Gewicht
587 g
Auflage
2013
Sprache
Englisch
ISBN
978-3-642-44374-9
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.
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.
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