Alois P. Heinz

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| We propose the lazy neural tree (LNT) as the appropriate architecture for the realization of smooth regression systems. The LNT is a hybrid of a decision tree and a neural network. From the neural network it inherits smoothness of the generated function , incremental adaptability, and conceptual simplicity. From the decision tree it inherits the topology(More)
We propose Adaptive Fuzzy Neural Trees as an appropriate tool for intelligent data analysis, comprehension , and prediction. Instead of using a single technique Adaptive Fuzzy Neural Trees as a mixture of paradigms combine the main advantages of neural networks, decision trees, and fuzzy logic. Like neural networks they are able to model smooth functions(More)
We propose a new class of artiicial neural networks for regression tasks and its construction algorithm. These networks have two diierent but isomorphic layouts. The tree-structured layout is used and built up during the construction phase and it can be used for accelerated serial evaluation of the network. The three-layer layout can be derived from the(More)