Muutke küpsiste eelistusi

E-raamat: Fuzzy Algorithms: With Applications To Image Processing And Pattern Recognition

(Hong Kong Polytechnic Univ, Hong Kong), (Univ Of Sydney, Australia), Edited by (Univ Of Sydney, Australia)
  • Formaat - PDF+DRM
  • Hind: 32,76 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This text deals with the subject of fuzzy algorithms and their applications to image processing and pattern recognition. Subjects covered include membership functions; fuzzy clustering; fuzzy rulers and defuzzification; fuzzy classifiers; and combined classifiers.
Introduction: Fuzzy sets; probability and fuzziness; fuzzy models.
Membership functions: heuristic selections; clustering approaches; adjustment
and toning; applications; concluding remarks. Fuzzy clustering: clustering
and fuzzy partition; fuzzy c-means algorithm; fuzzy cohonen clustering
networks; cluster validity and optimal fuzzy clustering; applications;
concluding remarks. Fuzzy rules and defuzzification: rules based on
experience; learning from examples; decision tree approach; neural network
approach; minimization of fuzzy rules; defuzzification and optimization;
applications; concluding remarks. Fuzzy classifiers: fuzzy nearest neighbour
classifier; fuzzy multilayer perceptron; fuzy decision trees; fuzzy string
matching; applications; concluding remarks. Combined clasifications:
introduction; voting schemes; maximum poteriori probability; Dempster-Shafer
evidence theory; trained perceptron neural networks; applications; concluding
remarks.