Aprendizado supervisionado usando redes neurais construtivas
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Data
2006-05-25Autor
Bertini Junior, João Roberto
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Constructive neural learning is a neural learning model that does not assume a fixed
network topology before training begins. The main characteristic of this learning model is the
dynamic construction of the network s hidden layers that occurs simultaneously with training.
This work investigates three topics related to constructive neural learning namely
algorithms for training an individual TLU, constructive neural algorithms for two class
problems and constructive neural algorithms for multiclass problems.
The first research topic is approached by discussing a few TLU training algorithms,
namely Perceptron, Pocket, Thermal, Modified Thermal, MinOver and BCP.
This work approaches constructive neural learning for two class classification tasks by
initially reviewing Tower, Pyramid, Tiling and Upstart algorithms, aiming at their multiclass
versions. Next five constructive neural algorithms namely Shift, Offset, PTI, Perceptron
Cascade and Sequential are investigated and two hybrid algorithms are proposed: Hybrid
Tiling, that does not restrict the TLU s training to only one algorithm and the OffTiling, a
collaborative approach based on Tiling and Offset.
Multiclass constructive neural learning was approached by investigating TLUs
training algorithms that deal with multiclass as well as by investigating multiclass versions of
Tower, Pyramid, Tiling, Upstart and Perceptron Cascade.
This research work also describes an empirical evaluation of all the investigated
algorithms conducted using several knowledge domains. Results are discussed and analyzed.