Details

Autor(en) / Beteiligte

• WANG, Shangfei
• HE, Menghua
• ZHU, Yachen
• HE, Shan
• LIU, Yue
• JI, Qiang

Titel

Learning with privileged information using Bayesian networks

Ist Teil von

• Frontiers of Computer Science, 2015-04, Vol.9 (2), p.185-199

Ort / Verlag

Heidelberg: Higher Education Press

Erscheinungsjahr

2015

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• For many supervised learning applications, additional information, besides the labels, is often available during training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network (BN) approach for learning with privileged information. We propose to incorporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classification. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach.