Skip to main content

2013 | OriginalPaper | Buchkapitel

2. Classification

verfasst von : Geoff Dougherty

Erschienen in: Pattern Recognition and Classification

Verlag: Springer New York

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

search-config
loading …

Abstract

Classification assigns objects to various classes based on measured features. The features are considered as a feature vector in feature space. It is important to select the most informative features and/or combine features for successful classification. Typically a sample set (the training set) is selected to train the classifier, which is then applied to other objects (the test set). Supervised learning uses a labeled training set, in which it is known to which class the objects belong, and is an inductive reasoning process. There are a variety of approaches to classification; statistical approaches, characterized by an underlying probability model, are very important. We will consider a number of robust features and examples based on shape, size, and topology to classify various objects.

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!

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!

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!

Literatur
Zurück zum Zitat Aha, D.: Feature weighting for lazy learning algorithms. In: Liu, H., Motoda, H. (eds.) Feature Extraction, Construction and Selection: A Data Mining Perspective, pp. 13–32. Kluwer, Norwell, MA (1998)CrossRef Aha, D.: Feature weighting for lazy learning algorithms. In: Liu, H., Motoda, H. (eds.) Feature Extraction, Construction and Selection: A Data Mining Perspective, pp. 13–32. Kluwer, Norwell, MA (1998)CrossRef
Zurück zum Zitat Anderson, J., Pellionisz, A., Rosenfeld, E.: Neurocomputing 2: Directions for Research. MIT, Cambridge, MA (1990) Anderson, J., Pellionisz, A., Rosenfeld, E.: Neurocomputing 2: Directions for Research. MIT, Cambridge, MA (1990)
Zurück zum Zitat Barto, A.G., Sutton, R.S.: Reinforcement learning in artificial intelligence. In: Donahue, J.W., Packard Dorsal, V. (eds.) Neural Network Models of Cognition, pp. 358–386. Elsevier, Amsterdam (1997)CrossRef Barto, A.G., Sutton, R.S.: Reinforcement learning in artificial intelligence. In: Donahue, J.W., Packard Dorsal, V. (eds.) Neural Network Models of Cognition, pp. 358–386. Elsevier, Amsterdam (1997)CrossRef
Zurück zum Zitat Batista, G., Monard, M.: An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17, 519–533 (2003)CrossRef Batista, G., Monard, M.: An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17, 519–533 (2003)CrossRef
Zurück zum Zitat Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000) Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Zurück zum Zitat Csiszar, I.: Maxent, mathematics, and information theory. In: Hanson, K.M., Silver, R.N. (eds.) Maximum Entropy and Bayesian Methods, pp. 35–50. Kluwer, Norwell, MA (1996)CrossRef Csiszar, I.: Maxent, mathematics, and information theory. In: Hanson, K.M., Silver, R.N. (eds.) Maximum Entropy and Bayesian Methods, pp. 35–50. Kluwer, Norwell, MA (1996)CrossRef
Zurück zum Zitat De Mantaras, R.L., Armengol, E.: Machine learning from examples: inductive and lazy methods. Data Knowl. Eng. 25, 99–123 (1998)CrossRefMATH De Mantaras, R.L., Armengol, E.: Machine learning from examples: inductive and lazy methods. Data Knowl. Eng. 25, 99–123 (1998)CrossRefMATH
Zurück zum Zitat Dubes, R.C., Jain, A.K.: Clustering techniques: the user’s dilemma. Pattern Recognit. 8, 247–290 (1976)CrossRef Dubes, R.C., Jain, A.K.: Clustering techniques: the user’s dilemma. Pattern Recognit. 8, 247–290 (1976)CrossRef
Zurück zum Zitat Fu, K.S.: Syntactic Pattern Recognition and Applications. Prentice-Hall, Englewood Cliffs (1982)MATH Fu, K.S.: Syntactic Pattern Recognition and Applications. Prentice-Hall, Englewood Cliffs (1982)MATH
Zurück zum Zitat Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85–126 (2004)CrossRefMATH Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85–126 (2004)CrossRefMATH
Zurück zum Zitat Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1475–1485 (2000) Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1475–1485 (2000)
Zurück zum Zitat Jensen, F.V.: An Introduction to Bayesian Networks. UCL Press, London (1996) Jensen, F.V.: An Introduction to Bayesian Networks. UCL Press, London (1996)
Zurück zum Zitat Markovitch, S., Rosenstein, D.: Feature generation using general constructor functions. Mach. Learn. 49, 59–98 (2002)CrossRefMATH Markovitch, S., Rosenstein, D.: Feature generation using general constructor functions. Mach. Learn. 49, 59–98 (2002)CrossRefMATH
Zurück zum Zitat Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)MATH Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)MATH
Zurück zum Zitat Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. SMC-9, 62–66 (1979) Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. SMC-9, 62–66 (1979)
Zurück zum Zitat Perlovsky, L.I.: Conundrum of combinatorial complexity. IEEE Trans. Pattern Anal. Mach. Intell. 20, 666–670 (1998)CrossRef Perlovsky, L.I.: Conundrum of combinatorial complexity. IEEE Trans. Pattern Anal. Mach. Intell. 20, 666–670 (1998)CrossRef
Zurück zum Zitat Ripley, B.: Statistical aspects of neural networks. In: Bornndorff-Nielsen, U., Jensen, J., Kendal, W. (eds.) Networks and Chaos - Statistical and Probabilistic Aspects, pp. 40–123. Chapman and Hall, London (1993) Ripley, B.: Statistical aspects of neural networks. In: Bornndorff-Nielsen, U., Jensen, J., Kendal, W. (eds.) Networks and Chaos - Statistical and Probabilistic Aspects, pp. 40–123. Chapman and Hall, London (1993)
Zurück zum Zitat Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)MATH Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)MATH
Metadaten
Titel
Classification
verfasst von
Geoff Dougherty
Copyright-Jahr
2013
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4614-5323-9_2