Machine Learning
Initializing neural networks using decision trees
Computational learning theory and natural learning systems: Volume IV
Geometrical synthesis of MLP neural networks
Neurocomputing
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Border pairs method - constructive MLP learning classification algorithm
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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In this paper we present the Border Pairs Method, a constructive learning algorithm for multilayer perceptron (MLP). During learning with this method a near-minimal network architecture is found. MLP learning is conducted separately by individual layers and neurons. The algorithm is tested in computer simulation with simple learning patterns (XOR and triangles image), with traditional learning patterns (Iris and Pen-Based Recognition of Handwritten Digits) and with noisy learning patterns. During the learning process we observed the following behaviour of BPM: capability to focus on global minima, good generalisation, no problems in learning with noisy, multi-dimensional and numerous learning patterns. The Border Pairs Method also supports incremental and online learning. Both are realized with or without MLP reconstruction and with or without forgetting (unlearning). The learning results with the BPM method are comparable with results from other methods.