This paper is concerned with relevance ranking in search, particularly that using term dependency information. It proposes a novel and unified approach to relevance ranking using ...
Abstract. Understanding the challenges faced by real projects in evolving variability models, is a prerequisite for providing adequate support for such undertakings. We study the e...
Rafael Lotufo, Steven She, Thorsten Berger, Krzysz...
In this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of non-stationarity such as session dependent variation, the recor...
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
We develop new techniques for time series classification based on hierarchical Bayesian generative models (called mixed-effect models) and the Fisher kernel derived from them. A k...