In this paper, we propose a conditional information analysis in which information on important patterns is selcctively detected. The selection is realized by a-information which can he used to maximize or minimize selectively conditional information, according to the important or characteristics of input patterns. The information analysis was applied to two feature detection problems: an alphabet character recognition aiid Iiieclical data. In both problems; experimental results confirmed that conditional information is flexibly maximized or minimized, depending upon input patterns. We coiild also see that coiiditional information is a good rneasiire to distinguish between different classes.
['Ryotaro Kaminiura']
Conditional Information Analysis