Buried Tag Identification with a new RBF Classifier

Beheim, Larbi; Zitouni, Adel; Belloir, Fabien

doi:10.1109/norsig.2006.275215

Cited by 1 publication

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“…However, it might break down without warning if the test samples are from different domains' datasets. Stimulating by the recent development of neural networks, we adopted the radial basis functions network (RBFN) [60,61] which is a multilayered perceptron model that is widely used in classification, regression, feature extraction, etc [62][63][64][65]. The main strategy of this approach is to transform the inverse mapping problem into calculating the linear weights of the radial basis functions (RBF), which enables a smooth and continuous reconstruction which hasn't been accomplished by other methods.…”

Section: Introductionmentioning

confidence: 99%

Application of radial basis functions neutral networks in spectral functions

et al. 2021

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The reconstruction of spectral function from correlation function in Euclidean space is a challenging task. In this paper, we employ the Machine Learning techniques in terms of the radial basis functions networks to reconstruct the spectral function from a finite number of correlation data. To test our method, we first generate one type of correlation data using a mock spectral function by mixing several Breit-Wigner propagators. We found that compared with other traditional methods, TSVD, Tikhonov, and MEM, our approach gives a continuous and unified reconstruction for both positive definite and negative spectral function, which is especially useful for studying the QCD phase transition. Moreover, our approach has considerably better performance in the low frequency region. This has advantages for the extraction of transport coefficients which are related to the zero frequency limit of the spectral function. With the mock data generated through a model spectral function of stress energy tensor, we find our method gives a precise and stable extraction of the transport coefficients.

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