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Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation...
A weighted least squares scheme based on an empirical survival error potential function is proposed in this paper. The empirical survival error potential function provides an error compensation scheme for noise distributions far from being Gaussian. This error compensation procedure is efficiently implemented via a weighted least squares formulation where an analytical solution form is obtained. The...
Feature selection techniques play a crucial role in machine learning tasks such as regression and classification. Many filter methods of feature selection are based on the mutual information (e.g. MIFS, MIFS-U, NMIFS, and mRMR methods). In this work, a new mutual information is defined based on the cross survival information potential (CSIP) and Cauchy-Schwartz divergence (CSD), called the survival...
In this paper, we define a new Mercer kernel, namely survival kernel, which is closely related to our recently proposed survival information potential (SIP). The new kernel function is parameter free, simple in calculation, and strictly positive-definite (SPD) over Rm+, hence it has potential utility in machine learning especially in online kernel learning. In this work we apply the survival kernel...
Recently, the information potential (IP) of order , defined as the argument of the log in the -order Renyi entropy, has been successfully used as an information theoretic criterion for supervised adaptive system training. In this paper, we use the survival function (or equivalently the distribution function) of an absolute value transformed random variable to define a new information...
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