Recently, El-Sherpieny et al (2020) suggested Type -II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regards being given to the choice priors and posterior risk associated with model. This paper fills this gap derived new LDmodel minimum posterior risk for selection priors. It derives a closed form expression Bayes estimates risks using Square error lo...

We propose an efficient online learning method of dialogue management based on Bayes risk criterion for document retrieval systems with a speech interface. The system has several choices in generating responses. So far, we have optimized the selection as minimization of Bayes risk based on reward for correct information presentation and penalty for redundant turns. In this paper, this framework...

Minimum Bayes-Risk (MBR) speech recognizers have been shown to give improvements over the conventional maximum a-posteriori probability (MAP) decoders through N-best list rescoring and A search over word lattices. Segmental MBR (SMBR) decoders simplify the implementation of MBR recognizers by segmenting the N-best lists or lattices over which the recognition is performed. We present a lattice c...

This work addresses two major drawbacks of the current cardiovascular risk score systems: reduced number of risk factors considered by each individual tool and the inability of these tools to deal with incomplete information. To achieve this goal a two phase strategy was followed. In the first phase, a common representation procedure was considered, based on a Naïve-Bayes classifier methodology...

Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent algorithms, i.e., the estimator converges in probability or in some stronger sense to the Bayes risk or to the Bayes decision function. Kernel methods based on minimizing the regularized risk over a reproducing kernel ...

Suppose Nature picks a probability measure P on a complete separable metric space X at random from a measurable set P fP g Then without knowing a statistician picks a measure Q on X Finally the statistician su ers a loss D P jjQ the relative entropy between P and Q We show that the minimax and maximin values of this game are always equal and there is always a minimax strategy in the closure of ...

Let X be a random variable taking values in a compact Riemannian manifold without boundary, and let Y be a discrete random variable valued in {0; 1} which represents a classification label. We introduce a kernel rule for classification on the manifold based on n independent copies of (X, Y ). Under mild assumptions on the bandwidth sequence, it is shown that this kernel rule is consistent in th...