Estimating the Shapes of Gravity Sources through Optimized Support Vector Classifier (SVC)

Bayesian Trigonometric Support Vector Classifier

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristics of natural normalization in the likelihood function, and then follow standard Gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implemen...

Estimating the support of a scaling vector

An estimate is given for the support of each component function of a compactly supported scaling vector satisfying a matrix refinement equation with finite number of terms. The estimate is based on the highest and lowest degree of each polynomial in the corresponding matrix symbol. Only basic techniques from matrix theory are involved in the derivation.

Estimating the Number of Wideband Radio Sources

In this paper, a new approach for estimating the number of wideband sources is proposed which is based on RSS or ISM algorithms. Numerical results show that the MDL-based and EIT-based proposed algorithm havea much better detection performance than that in EGM and AIC cases for small differences between the incident angles of sources. In addition, for similar conditions, RSS algorithm offers hi...

Bayesian Inference in Trigonometric Support Vector Classifier

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristics of natural normalization in the likelihood function, and describe a Bayesian framework in stationary Gaussian stochastic processes. In this framework, Bayesian inference is used to implement model ...

Support vector classifier with asymetric kernel function