| 摘要 |
<p>During learning, when a learning signal that includes normal samples and abnormal samples is inputted to a feature value extraction unit (2), the learning signal is short-time Fourier transformed and learning data is extracted. For each combination of time and frequency, a classifier creation unit (6) uses decision results from a learning decision unit (4) to create a classifier that minimizes an erroneous-decision rate as computed by a computation unit (5). From among the classifiers, which were created for each time/frequency combination, a classifier selection unit (7) selects the classifier with the lowest erroneous-decision rate and computes the reliability. In response to decision results from the selected classifier, a weighting instruction unit (31) instructs a weight setting change unit (30) to change the weights of the learning data. During examination, an examination decision unit (8) uses the plurality of classifiers selected during learning to decide whether or not the examination subject is in a normal state.</p> |
