A fast and compact classifier based on sorting in an iteratively expanded input space
✍ Scribed by Radu Dogaru; Manfred Glesner
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 210 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0884-8173
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✦ Synopsis
This paper proposes a compact neural classifier, based on the theory of simplicial decomposition and approximation, with a very convenient hardware or software implementation. It can learn arbitrary n-inputs patterns with O(n) time complexity. There are no multipliers required, and the learned knowledge is stored in a general purpose RAM with a size ranging from O(n) to O(n 2 ). The proposed architecture is composed only of four building blocks, an iterative nonlinear expander, a sorter, a RAM memory, and an accumulator, all of them readily available in either digital hardware or software technology. Simulation results indicate good accuracy for a wide variety of benchmark problems.