Mittagsseminar (in cooperation with J. Lengler, A. Steger, and D. Steurer)

Date and Time: Thursday, August 22, 2013, 12:15 pm

Duration: 30 minutes

Location: OAT S15/S16/S17

Speaker: Christoph Schwirzer

Learning object classification fast

This talk is about the problem of learning object classification using a small training set. We examine the problem of recognizing handwritten characters. Using a simple neural network we achieve a classification rate of more than 87% on handwritten decimal digits after learning as few as 20 samples per digit only. Based on a similar model, we examine the classification of simple geometric figures. Starting with a long and unsupervised learning stage the similarity of objects is learned. A following short and supervised learning stage is shown to be enough to perfectly distinguish certain basic geometric figures like squares and diamonds.

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