The basic ideas in neural networks
Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast learning algorithm for deep belief nets
Neural Computation
Breast cancer diagnosis using genetic programming generated feature
Pattern Recognition
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
A generic optimising feature extraction method using multiobjective genetic programming
Applied Soft Computing
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We use neuroevolution to construct nonlinear transformation functions for feature construction that map points in the original feature space to augmented pattern vectors and improve the performance of generic classifiers. Our research demonstrates that we can apply evolutionary algorithms to both adapt the weights of a fully connected standard multi-layer perceptron (MLP), and optimize the topology of a generalized multi-layer perceptron (GMLP). The evaluation of the MLPs on four commonly used data sets shows an improvement in classification accuracy ranging from 4 to 13 percentage points over the performance on the original pattern set. The GMLPs obtain a slightly better accuracy and conserve 14% to 54% of all neurons and between 40% and 89% of all connections compared to the standard MLP.