Selective Information Acquisition with Application to Pattern Classification

In this paper, we pr p se a selective information acquisition device which can be used to store information selectively according to the important or characteristics of input patterns. For selective information acquisition, we introduce _-information to distort the ordinary Shannon information function. We have no choice but to maximize or minimize information to eliminate this distortion. Thus, the distortion elimination can be employed as a basic mechanism of the device to maximize or minimize information selectively. The information device is applied to a phonological feature detection problem. In this problem, experimental results confirmed that conditional information is flexibly maximized or minimized, depending upon input patterns. We could also see that conditional information is a good measure to distinguish between different classes, and that the strength of conditional information is used to classify input patterns.