We consider the problem of building visual anomaly detection systems for mobile robots.
So far, ML were engaged to create CLs and values on the edges of these CLs expressing ML preferences at solution insertion.
The ML-Constructive heuristic is a recently presented method and the first hybrid method capable of scaling up to real scale traveling salesman problems.
Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0. 35% error rate on the famous MNIST handwritten digits benchmark.