Natural Gradient and Multiclass NLDA Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Architecture Selection in NLDA Networks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Natural Gradient Learning in NLDA Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Natural learning in NLDA networks
Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Presents a nonlinear supervised feature extraction algorithm that combines Fisher's criterion function with a preliminary perceptron-like nonlinear projection of vectors in pattern space. Its main motivation is to combine the approximation properties of multilayer perceptrons (MLPs) with the target free nature of Fisher's classical discriminant analysis. In fact, although MLPs provide good classifiers for many problems, there may be some situations, such as unequal class sizes with a high degree of pattern mixing among them, that may make difficult the construction of good MLP classifiers. In these instances, the features extracted by our procedure could be more effective. After the description of its construction and the analysis of its complexity, we illustrate its use over a synthetic problem with the above characteristics