Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 4/2009

01.12.2009 | Theoretical Advances

Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas

verfasst von: Konrad Jackowski, Michal Wozniak

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2009

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The paper presents the novel adaptive splitting and selection algorithm (AdaSS) used for learning compound pattern recognition system. Splitting a feature space into its constituents and selection of the best area classifier from the pool of available recognizers for each region are key processes of the proposed model. Both take place simultaneously as part of a compound optimization process aimed at maximizing system performance. Evolutionary algorithms are used to find out the optimal solution. The results of experiments for algorithm evaluation purposes prove the quality of the proposed approach.
Vorheriger Artikel Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images
Nächster Artikel Phrase classes in two-level language models for ASR

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Jain AK, Duin PW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans PAMI 22(1):4–37
2.
Chow CK (1965) Statistical independence and threshold functions. IEEE Trans Electron Comput EC-16:66–68CrossRef
3.
Krzanowski W, Partrige D (1996) Software diversity: practical statistics for its measurement and exploitation. Department of Computer Science, Raport University of Exeter
4.
Kuncheva LI, Whitaker CJ (2001) Ten measures of diversity in classifier ensembles: limits for two classifiers. In: IEEE workshop on intelligent sensor processing, Birmingham, pp. 10/1–10/6
5.
Xu L, Krzyżak A, Suen Ch Y (1992) Methods of combining multiple classifiers and their applications to handwritting recognition. IEEE Trans Syst Man Cybern 22(3):418–435CrossRef
6.
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New JerseyMATHCrossRef
7.
Ruta D, Gabrys B (2005) Classifier selection for majority voting. Inf Fusion 6:63–81CrossRef
8.
Kuncheva LI, Whitaker CJ, Shipp CA, Duin RPW (2003) Limits on the majority vote accuracy in classifier fusion. Pattern Anal Appl 6:22–31MATHCrossRefMathSciNet
9.
Hashem S (1997) Optimal linear combinations of neural networks. Neural Netw 10(4):599–614CrossRef
10.
Kuncheva LI, Jain LC (2000) Designing classifier fusion systems by genetic algorithms. IEEE Trans Evol Comput 4:327–336CrossRef
11.
Inoue H, Narihisa H (2002) Optimizing a multiple classifier systems. Lect Notes Comput Sci 2417:285–294CrossRef
12.
Wozniak M (2007) Experiments with trained and untrained fusers. In: Corchado E, Corchado JM, Abraham A (eds) Innovations in hybrid intelligent systems. Springer, Berlin, pp 144–150
13.
Rao NSV (2004) A generic sensor fusion problem: classification and function estimation. LNCS 3077:16–30
14.
Fumera G, Roli F (2005) A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 27(6):942–956CrossRef
15.
Biggio B et al (2007) Bayesian analysis of linear combiners. LNCS 4472:292–301
16.
Tumer K, Ghosh J (1999) Linear and order statistics combiners for pattern classification. In: Sharkley AJC (ed) Combining artificial neural networks. Springer, Berlin, pp 127–155
17.
Duin RPW (2002) The combining classifier: to train or not to train? In: Proceedings of the ICPR2002, Quebec City
18.
Rastrigin LA, Erenstein RH (1981) Method of collective recognition. Energoizdat, MoscowMATH
19.
Baram Y (1998) Partial classification: the benefit of deferrer decision. IEEE Trans Pattern Anal Mach Intell 20(8):769–776CrossRefMathSciNet
20.
Cordella LP, Foggia P, Sansone C, Tortorella F, Vento M (2000) A cascade multiple expert system for verification. Lect Notes Comput Sci 1857:330–339CrossRef
21.
Giacinto G, Roli F, Fumera G (2000) Design of effective multiple classifier systems by clustering of classifiers. In: Proceedings of 15th international conference on pattern recognition—ICPR’00, vol 2, Barcelona, Spain, pp 160–163
22.
Goebel K, Yan W (2004) Choosing classifier for decision fusion. In: Proceedings of the 7th international conference on information fusion, Stockholm, Sweden, pp 562–568
23.
Kuncheva LI (2000) Cluster-and-selection method for classifier combination. In: Proceedings of the 4th international conference on knowledge-based intelligent engineering systems and allied technologies, Brighton, pp 185–188
24.
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRef
25.
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley-Interscience, New JerseyMATH
26.
Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice Hall, LondonMATH
27.
Higleyman WH (1962) The design and analysis of pattern recognition experiments. Bell Syst Tech J 41:723–744
28.
Kuratowski K, Mostowski A (1996) Set theory. North-Holland Pub. Co, Amsterdam
29.
Rokach L (2006) Decomposition methodology for classification tasks—a meta decomposer framework. Pattern Anal Appl 9(2–3):257–271MathSciNet
30.
Gabrys B, Ruta D (2006) Genetic algorithms in classifier fusion. Appl Soft Comput J 6(4):337–347CrossRef
31.
32.
Duin RPW, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax DMJ (2004) PRTools4, a matlab toolbox for pattern recognition. Delft University of Technology
33.
Siegler RS (1976) Three aspects of cognitive development. Cognit Psychol 8:481–520
34.
35.
Duch W, Grudziński K (2001) Ensembles of similarity-based model. In: Advances in soft computing. Springer, Heidelberg, pp 75–85
36.
Duin RPW, Tax DMJ, Experiments with Classifier Combining Rules, Lecture Notes in Computer Science, No. 1857, 2000, pp 16–29
37.
Kuncheva LI, Bezdek JC, Duin RPW (2001) Decision templates for multiple classifier fusion: an experimental comparision. Pattern Recognition 34:299–314MATHCrossRef
38.
Van Erp M, Vuurpijl LG, Schomaker LRB, An overview and comparison of voting methods for pattern recognition. In: Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR.8), Niagara-on-the-Lake, Canada, pp 195–200
Metadaten
Titel
Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas
verfasst von
Konrad Jackowski
Michal Wozniak
Publikationsdatum
01.12.2009
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 4/2009
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-008-0137-7

Weitere Artikel der Ausgabe 4/2009

Pattern Analysis and Applications 4/2009 Zur Ausgabe

Theoretical Advances

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Theoretical Advances

Speeding up simplification of polygonal curves using nested approximations

Theoretical Advances

Phrase classes in two-level language models for ASR

Theoretical Advances

Finding regions of interest for cancerous masses enhanced by elimination of linear structures and considerations on detection correctness measures in mammography

Theoretical Advances

Respiratory sound classification by using an incremental supervised neural network

Theoretical Advances

Computationally efficient eigenspace decomposition of correlated images characterized by three parameters