Trio learning: a new strategy for building hybrid neural trees

Neural trees are constructive algorithms which build decision trees whose nodes are binary neurons. We propose a new learning scheme, "trio-learning," which leads to a significant reduction in the tree complexity. In this strategy, each node of the tree is optimized by taking into account the knowledge that it will be followed by two son nodes. Moreover, trio-learning can be used to build hybrid trees, with internal nodes and terminal nodes of different nature, for solving any standard tasks (e.g. classification, regression, density estimation). Significant results on a handwritten character classification are presented.