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
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2015

01.06.2015 | Original Article

Hybrid neural network for classification of graph structured data

verfasst von: R. B. Gnana Jothi, S. M. Meena Rani

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2015

Einloggen

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

search-config
loading …

Abstract

Multilayer perceptron (MLP) with recurrent architecture is proposed for stabilizing the state vector, which represents the characteristics of the nodes in a graph, to classify the graph structured data. M number of input and output networks are constructed for the M node undirected graphs for classifying graph structured data. Output of every input network represents the characteristics of the node as a state vector. The output of each input MLP is also taken as input for the same network along with output of neighboring node’s MLP. Both the input and output networks are trained by backpropagation. The proposed approach is implemented on the standard benchmark classification problems namely mutagenesis problem, subgraph matching problem and clique problem. Simulation results show that best accuracy in classification is obtained with minimum computational complexity.
Vorheriger Artikel A kind of approximations of generalized rough set model
Nächster Artikel Computational reasoning based on complemented distributive lattices

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
Weitere Produktempfehlungen anzeigen
Literatur
1.
Atiya A (1988) Learning on a general frame work, neural information processing systems. In: Anderson DZ (ed) AIP, New York, pp 22–30
2.
Bianchini M, Maggini M, Sarti L, Scarselli F (2005) Recursive neural networks for processing graphs with labeled edges: theory and applications. Neural Netw 18:1040–1050CrossRef
3.
Bianchini M, Gori M, Sarti M, Scarselli F (2006) Recursive processing of cyclic graphs. IEEE Trans Neural Netw 17(1):10–17.CrossRef
4.
Bong DBL, Tan JYB, Lai KC (2008) Application of multilayer perceptron with backpropagation algorithm and regression analysis for long-term forecast of electricity demand—a comparison. Int Conf Electron Des 3:1–5
5.
Chatrand G (2006) Introduction to Graph Theory. Tata Mc Graw-Hill, New Delhi
6.
De Raedt L, Blockeel H (1997) Using logical desicion trees for clustering. Lecture notes in artificial intelligence, vol 1297. pp 133–141
7.
Debnath AK, Lopex de Compandre R, Debnath G, Schusterman A, Hansch C (1991) Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds correlation with molecular orbital energies and hydrophobicity. J Med Chem 34:786–797CrossRef
8.
9.
Frasconi P, Gori M, Sperduti A (1998) A general framework for adaptive processing of data structures. IEEE Trans Neural Netw 9:768–786CrossRef
10.
Frasconi P, Gori M, Sperduti A (1997) On the efficient classification of data structures by neural networks. In: Proceedings of Fifteenth International Joint Conference Artificial Neural Networks, vol 2. pp 1066–1071
11.
Jain SK, Patnaik A, Sinha SN (2013) Design of custom made stacked patch antennas: a machine learning algorithm. Int J Mach Learn Cybern 4:189–194CrossRef
12.
Khotanzad A, Chung C (1998) Application of multilayer perceptron neural networks to vision problems. Neural Comput Appl 7:249–159CrossRef
13.
Kirsten M (2002) Multirelational distance-based clustering. Ph.D dissertation, School of computer Science, Otto-von-Guericke University, Magdeburg, Germany
14.
Noi LD, Hagenbuchner M, Scarselli F, Tsoi AC (2010) Web spam detection by probability mapping graph self organising maps and graph neural networks, Artificial Neural Networks, ICANN 2010. Lecture notes in computer science, vol 6353. pp 372–381
15.
Pollack JB (1990) Recursive distributed representations. Artifi Intell 46:77–106CrossRef
16.
Pucci A, Gori M, Hagenbuchner M, Scarselli F, Tsoi AC (2006) Applications of graph neural networks to large-scale recommender systems some results. In: Proceedings of International Multiconference on Computer Science and Information Technology, pp 189–195
17.
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning reprsentations by back-propagation errors. Nature 323:533–536CrossRef
18.
Savitha R, Suresh S, Sundarajan N (2013) Fast learning complex valued classifiers for real world classification problems. Int J Mach Learn Cybern 4:469–476CrossRef
19.
Sperduti A (1998) Neural networks for processing data structures, Adaptive processing of sequences and data structures. Lecture Notes in Computer Science, vol 1387. pp 121–144
20.
Sperduti A, Starita A, Goller C (1995) Learning distributed representations for the classification of terms. In: Proceedings of International Joint Conference on Artificial Intelligence, pp 509–515
21.
Sperduti A, Starita T (1997) Supervised neural networks for classification of structures. IEEE Trans Neural Netw 8:714–735CrossRef
22.
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20:61–80CrossRef
23.
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) Computational capabilities of graph neural networks. IEEE Trans Neural Netw 20:81–102CrossRef
24.
Scarselli F, Yong SL, Gori M, Hagenbuchner M, Tsoi AC, Maggini M.(2005) Graph neural networks for ranking web pages. In: Proceedings of the 2005 IEEE/WIC/ACM Conference on Web Intelligence, Washington DC, pp 666–672
25.
Srinivasan A, Muggleton S, King R, Sterbberg M (1994) Mutagenesis: Ilp experiments in a non-determinate biological domain. In: Proceedings of 4th International Workshop Inductive logic Programme, pp 217–232
26.
Tsang E, Wang X, Yeung D (2000) Improving learning accuracy of fuzzy decision trees by hybrid neural networks. IEEE Trans Fuzzy Syst 8:601–614CrossRef
27.
Wang X, Dong C, Fan T (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70:2581–2587CrossRef
Metadaten
Titel
Hybrid neural network for classification of graph structured data
verfasst von
R. B. Gnana Jothi
S. M. Meena Rani
Publikationsdatum
01.06.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0230-8

Weitere Artikel der Ausgabe 3/2015

International Journal of Machine Learning and Cybernetics 3/2015 Zur Ausgabe

Original Article

A kind of approximations of generalized rough set model

Original Article

A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement

Original Article

Linear discriminant analysis for the small sample size problem: an overview

Original Article

Synchronization of a class of memristive neural networks with time delays via sampled-data control

Original Article

Topological approaches to generalized definitions of rough multiset approximations

Original Article

A data-driven study for evaluating fineness of cement by various predictors

Neuer Inhalt

    Bildnachweise