We give a classical algorithm for linear regression analogous to the quantum matrix inversion [Harrow, Hassidim, and Lloyd, Physical Review Letters'09, arXiv:0811.3171] low-rank matrices [Wossnig, Zhao, Prakash, Letters'18, arXiv:1704.06174], when input $A$ is stored in data structure applicable QRAM-based state preparation. Namely, suppose we are given an $A \in \mathbb{C}^{m\times n}$ with mi...

At present, the prevalence of diabetes is increasing because human body cannot metabolize glucose level. Accurate prediction patients an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection a key concept in preprocessing. Thus, features that are relevant used for prediction. ...

In this paper, we introduce a new online algorithm for orthogonal regression. The method is constructed via a stochastic gradient descent approach combined with the idea of a tube loss function, which is similar to the one used in support vector (SV) regression. The algorithm can be used in primal or in dual variables. The latter formulation allows the introduction of kernels and soft margins. ...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...