The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
When monitoring spatial phenomena, which are often modeled as Gaussian Processes (GPs), choosing sensor locations is a fundamental task. A common strategy is to place sensors at t...
This work is concerned with the estimation of a classifier's accuracy. We first review some existing methods for error estimation, focusing on cross-validation and bootstrap,...
Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints ? mu...
Abstract. We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consi...
Sauro Menchetti, Andrea Passerini, Paolo Frasconi,...