ECAI 2004 Conference Paper

[full paper]

Visual Learning by Set Covering Machine with Efficient Feature Selection

Hiroki Nomiya, Kuniaki Uehara

In this paper, we propose a new visual learning method for real-world object recognition task. Our method is based on the Set Covering Machine (SCM), to make the learning time shorter than the methods based on commonly used trial-and-error algorithms, such as genetic programming and reinforcement learning. Generally, the process of visual learning is quite time-consuming because image data consists of large amount of information. We attempt to reduce the learning time by introducing the effective feature selection method to find small number of useful features in image data. Additionally, we introduced a criterion based on the Minimum Description Length (MDL) principle to refine the hypothesis. We perform some experiments to verify the effectiveness of our method.

Keywords: visual learning, Set Covering Machine, attribute selection, image filtering, adaptive learning

Citation: Hiroki Nomiya, Kuniaki Uehara: Visual Learning by Set Covering Machine with Efficient Feature Selection. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.525-529.