175,18 €
206,09 €
Uncertainty Modeling for Data Mining
Uncertainty Modeling for Data Mining
175,18 €
206,09 €
  • Išsiųsime per 10–14 d.d.
Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorith…
175.18 2025-07-06 23:59:00
  • Extra -15 % nuolaida šiai knygai su kodu: ENG15

Uncertainty Modeling for Data Mining + nemokamas atvežimas! | knygos.lt

Atsiliepimai

(4.00 Goodreads įvertinimas)

Aprašymas

Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.

Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.

EXTRA 15 % nuolaida

175,18 €
206,09 €
Išsiųsime per 10–14 d.d.

Kupono kodas: ENG15

Akcija baigiasi už 1d.00:05:13

Nuolaidos kodas galioja perkant nuo 10 €. Nuolaidos nesumuojamos.

Prisijunkite ir už šią prekę
gausite 2,06 Knygų Eurų!?
Įsigykite dovanų kuponą
Daugiau

Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.

Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.

Atsiliepimai

  • Atsiliepimų nėra
0 pirkėjai įvertino šią prekę.
5
0%
4
0%
3
0%
2
0%
1
0%
× promo banner