Kernel-based topographic map formation achieved with normalized Gaussian competition
Tóm tắt
A new learning algorithm for kernel-based topographic map formation is introduced. The kernels are Gaussians, and their centers and ranges individually adapted so as to yield an equiprobabilistic topographic map. The converged map also generates a heteroscedastic Gaussian mixture model of the input density. This is verified for both synthetic and real-world examples, and compared with other algorithms for kernel-based topographic map formation.
Từ khóa
#Kernel #Neurons #Lattices #Entropy #Clustering algorithms #Laboratories #Psychology #Neural networks #Maximum likelihood estimation #Marine vehiclesTài liệu tham khảo
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