Multiple paradigms, comparative studies and hybrid systems : by Edzard S Gelsema; International Workshop on Pattern

By Edzard S Gelsema; International Workshop on Pattern Recognition in Practice

The forty two papers during this booklet are sourced from a wide diversity of overseas experts curious about constructing development acceptance methodologies and people utilizing trend attractiveness thoughts of their specialist paintings. The ebook is split into six sections: trend acceptance; sign and photograph Processing; Probabilistic Reasoning; Neural Networks; Comparative reports; and Hybrid structures, giving potential clients a sense for the applicability of a few of the equipment of their specific box of specialization

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Additional info for Multiple paradigms, comparative studies and hybrid systems : proceedings of an international workshop held on Vlieland, the Netherlands, 1 - 3 June 1994

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Thus, we can associate with each contour a sequence of features and HMMs can be used for training and classification purposes. 6. E X P E R I M E N T A L RESULTS In the experiments we choose four different objects (see Figure 3). Using a training data set of 50 input images per object, different types of HMMs are trained from the sequences of extracted affine invariant features. We compute both Κχ for each contour. The classification results using 10 images of each object which are not included in the sample set are shown in Table 1.

Landgrebe. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics, 21(3):660-674, May/June 1991. 6. S. Michalski. Learning strategies and automatic knowledge acquisition. In L. Bole, editor. Computational Models of Learning, pages 1-19. Springer Verlag, 1987. 7. R. Quinlan. Decision trees and multi-valued attributes. E. Hayes, D. Mitchie, and J. Richards, editors, Machine Intelligence, volume 11, pages 305-318. Oxford University Press, Oxford, 1988. 8.

R. Quinlan. Decision trees as probabilistic classifiers. In Proceedings of the 4^h International Workshop on Machine Learning, pages 31-37, Irvine, CA, June 1987. Morgan Kaufman. 19. L. H. A. J. Stone. Classification and Regres­ sion Trees. Wadsworth, Belmont, CA, 1984. 20. Τ. Niblett. Constructing decision trees in noisy domains. In I. Bratko and N. Lavrac, editors. Progress in Machine Learning: Proceedings of the 2nd European Working Session on Learning, pages 67-78. Sigma Press, Bled, Yugoslavia, 1987.

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