Papers by Author: Xiao Hong Hu

Abstract: E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

Abstract: Graph based learning has been an active research topic in machine learning community as well as many application areas including image annotation recently. In order to exploit the correlation between keywords and images, we proposed a novel image annotation method via graph based learning and semantic fusion to estimate the probability of keywords being the caption of an image, and present a new framework to solve the problem. The experiments over Corel images have shown that this approach outperforms other methods and is effective for image annotation.