Dendrite morphological neurons trained by stochastic gradient descent

Dendrite morphological neurons are a type of artificial neural network that works with min and max operators instead of algebraic products. These morphological operators build hyperboxes in N-dimensional space. These hyperboxes allow the proposal of training methods based on heuristics without using an optimisation method. In literature, it has been claimed that these heuristic-based trainings have advantages: there are no convergence problems, perfect classification can always be reached and training is performed in only one epoch. In this paper, we show that these assumed advantages come with a cost: these heuristics increase classification errors in the test set because they are not optimal and learning generalisation is poor. To solve these problems, we introduce a novel method to train dendrite morphological neurons based on stochastic gradient descent for classification tasks, using these heuristics just for initialisation of learning parameters. Experiments show that we can enhance the testing error in comparison with solely heuristic-based training methods. This approach can reach competitive performance with respect to other popular machine learning algorithms.