Abstract
Conjoined data is data in which the classes abut but do not overlap. It is difficult to determine the boundary between the classes, as there are no inherent clusters. As a result traditional classification methods, such as Counter-Propagation networks, may underperform. This paper describes a modified Counter-Propagation network that is able to refine the boundary definition and so perform better when classifying conjoined data. The efficiency with which network resources are used suggests that it is worthy of consideration for classifying all kinds of data, not just conjoined data.
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References
Bishop CM (1997) Neural Networks: A Pattern Recognition Perspective. Institute of Physics Publishing & Oxford University Press; (B6): E. Fiesler, and R. Beale, (eds). In: Handbook of Neural Computation. ISBN: 0 7503 0312 3
Hecht-Nielsen R (1987) Counterpropagation Networks. Applied Optics 26(23):4979–4984
Moody J, Darken CJ (1989) Fast learning in networks of locally turned processing units. Neural Computation 1:281–294
NeuralWare (1993) Neural computing; A technology handbook for professional II/PLUS and neuralWorks explorer. Pittsburgh, PA: NeuralWare
Selim SZ, Ismail MA (1984) K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence. 6(1):81–87
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Pierrot, H., Hendtlass, T. Using a modified counter-propagation algorithm to classify conjoined data. Appl Intell 24, 241–251 (2006). https://doi.org/10.1007/s10489-006-8515-6
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DOI: https://doi.org/10.1007/s10489-006-8515-6