Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Robustness of radial basis functions
Neurocomputing
An on-chip-trainable Gaussian-Kernel analog support vector machine
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Kerneltron: support vector "machine" in silicon
IEEE Transactions on Neural Networks
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A simple and area-efficient differential Gaussian circuit is presented for machine learning dedicated hardware implementations, where Gaussian functions are needed, e.g. for artificial neural networks (as transfer function), support vector machines (as kernel function) and fuzzy logic (as membership function). The proposed Gaussian circuit consists of only 4 transistors. Simulations in the 0.18-@mm CMOS UMC technology show that the proposed circuit is more accurate, less susceptible to the process variations and requires less on-chip area when compared to state-of-the-art.