초록 ▼

To prevent overfitting a neural network to a finite set of training samples, random distortions are dynamically applied to the samples each time they are applied to the network during a training session. A plurality of different types of distortions can be applied, which are randomly selected each t

To prevent overfitting a neural network to a finite set of training samples, random distortions are dynamically applied to the samples each time they are applied to the network during a training session. A plurality of different types of distortions can be applied, which are randomly selected each time a sample is applied to the network. Alternatively, a combination of two or more types of distortion can be applied each time, with the amount of distortion being randomly varied for each type.

대표청구항 ▼

[ What is claimed is:] [1.] A method for training a statistical classifier to recognize input patterns that belong to respective predetermined classes, utilizing a set of training samples which are respectively associated with said classes, comprising the following steps which are repeated over a la

[ What is claimed is:] [1.] A method for training a statistical classifier to recognize input patterns that belong to respective predetermined classes, utilizing a set of training samples which are respectively associated with said classes, comprising the following steps which are repeated over a large number of iterations:selecting a training sample from said set of training samples;producing a set of distortion parameters;selectively distorting said training sample in accordance with said distortion parameters to compute a classifier input pattern; andtraining the classifier using said classifier input pattern.