Bayes and Naive-Bayes Classifier

Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise...

PAC-Bayes risk bound combining Bayesian theory and structure risk minimization for stochastic classifiers has been considered as a framework for deriving some of the tightest generalization bounds. A major issue for calculating the bound is the unknown prior and posterior distributions of the concept space. In this paper, we formulated the concept space as Reproducing Kernel Hilbert Space (RKHS...

We propose a local boosting method in classification problems borrowing from an idea of the local likelihood method. The proposed method includes a simple device to localization for computational feasibility. We proved the Bayes risk consistency of the local boosting in the framework of PAC learning. Inspection of the proof provides a useful viewpoint for comparing the ordinary boosting and the...

Sparsity is a fundamental topic in highdimensional data analysis. Perhaps the most common measures of sparsity are the `norms, for 0 ≤ p < 2. In this paper, we study an alternative measure of sparsity, the truncated `-norm, which is related to other `-norms, but appears to have some unique and useful properties. Focusing on the ndimensional Gaussian location model, we derive exact asymptotic mi...

This paper describes the development of a failure diagnosis technique for V-belts through vibration monitoring. The V-belt vibration is monitored at a driven bearing body attached to a power transmission device. Seven basic causes of belt failure and their combinations are considered. Power spectra of the vibration data are calculated through noise reduction by a cross-spectrum method. Six para...

We propose an efficient and accurate approximate Bayesian Markov chain Monte Carlo methodology for the estimation of event rates under an overdispersed Poisson distribution. A Gibbs sampling algorithm is derived, based on a log-normal/gamma mixture density that closely approximates the conditional distribution of the Poisson parameters. This involves a moment matching process, with the exact co...