Bayes and empirical Bayes methods have proven eeective in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining overall geographic trends and patterns. Recent w ork applies these methods to the analysis of areal data which are spatially misaligned, i.e., involving variables (typically counts or rates) which are aggregated over d...

The currently dominant speech recognition technology, hidden Markov modeling, has long been criticized for its simplistic assumptions about speech, and especially for the naive Bayes combination rule inherent in it. Many sophisticated alternative models have been suggested over the last decade. These, however, have demonstrated only modest improvements and brought no paradigm shift in technolog...

In this paper, we propose a semantic naïve Bayes classifier (SNBC) to improve the conventional naïve Bayes classifier (NBC) by incorporating “document-level” semantic information for document classification (DC). To capture the semantic information from each document, we develop semantic feature extraction and modeling algorithms. For semantic feature extraction, we first apply a log-Bilinear d...

We present an algorithm for learning correlations among link types and node attributes in relational data that represent complex networks. The link correlations are represented in a Bayes net structure. This provides a succinct graphical way to display relational statistical patterns and support powerful probabilistic inferences. The current state of the art algorithm for learning relational Ba...

In this paper, we show how using the Dirichlet Process mixture model as a generative model of data sets provides a simple and effective method for transfer learning. In particular, we present a hierarchical extension of the classic Naive Bayes classifier that couples multiple Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters and show how recent advances in appro...

We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification problems. The classification tree is constructed from empirical estimates of the Henze-Penrose bounds on the pairwise Bayes misclassification rates that rank the binary subproblems in...

This paper overviews Bayesian approaches in natural language processing that are becoming prominent. Without any knowledge of natural language processing, Bayesian approaches to both discriminative learning and generative modeling are described. Especially, näıve bayes and its full unsupervised Bayesian modeling, DM, and LDA are developed. These Bayesian approaches permit interesting joint mode...

Traditionally, decision trees rank instances by using the local probability estimations for each leaf node. The instances in the same leaf node will be estimated with equal probabilities. In this paper, we propose a hierarchical ranking strategy by combining decision trees and leaf weighted Näıve Bayes to improve the local probability estimation for a leaf node. We consider the importance of th...

We present Adaptive Bayes, an adaptive incremental version of Naïve Bayes, to model a prediction task based on learning styles in the context of an Adaptive Hypermedia Educational System. Since the student’s preferences can change over time, this task is related with a problem known as concept drift in the machine learning community. For this class of problems an adaptive predictive model, able...

We begin by recalling the core Bayesian hierarchical modeling result of Lindley and Smith (1972), henceforth abbreviated L&S. For n × p1-dimensional response vector z, p1-vector of parameters θ, known n× p1 design matrix A1, and known n× n covariance matrix C1, let the likelihood be z ∼ N(A1θ, C1). Then for second-level p2-vector of parameters μ, known design and covariance matrices A2 and C2, ...