Classification problems involving signals can benefit from the application of subspace neural networks. In order to fully exploit them, a constructive approach based on learning theory is mandatory. A possible method having these characteristics is proposed in the present contribution. It yields satisfactory performances, as illustrated by the results obtained with the well-known sonar benchmark.
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