A Data Parititioning Approach to speed up the Fuzzy ARTMAP algorithm using the Hilbert space-filling Curve

Author(s): Castro, J. | Demara, R. | Georgiopoulos, M. |

Year: 2004

Citation: Proceedings, International Joint Conference on Neural Networks, Budapest, Hungary.

Abstract: One of the properties of FAM, which is a mixed blessing, is its capacity to produce new neurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure, but it also has the undesirable side effect that on large databases it can produce a large network size that can dramatically slow down the algorithms training time. To address this problem we propose the use of the Hilbert space-filling curve. Our results indicate that the Hilbert spacefilling curve can reduce the training time of FAM hy partitioning the learning set without a significant effect on the classification performance or network size. Given that there is full data partitioning with the HSFC we implement and test a parallel implementation on a Beowulf cluster of workstations that further speeds up the training and classification time on large databases.

Topics: Machine Learning, Models: Fuzzy ARTMAP,

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Cross References

  1. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
    A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may ... Article Details

  2. Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system
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