| 摘要 |
A pattern recognition method and apparatus decrease the amount of computation for pattern recognition and adapts flexibly to an increase and a change in learning samples. Learning is made beforehand on base vectors in a subspace of each category and a kernel function. Pattern data to be recognized is input, and projection of an input pattern to a nonlinear subspace of each category is decided. Based on the decided projection, a Euclidean distance or an evaluation value related to each category is calculated from the property of the kernel function, and is compared with a threshold value. If a category for which the evaluation value is below the threshold value exists, a category for which the evaluation value is the smallest is output as a recognition result. If there is no category for which the evaluation value is below the threshold value, a teaching signal is input for additional learning.
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