Donald E. Waagen

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Evolutionary programming, a systematic multi-agent stochastic search technique, is used to generate recurrent perceptrons (nonlinear IIR filters). A hybrid optimization scheme is proposed that embeds a single-agent stochastic search technique, the method of Solis and Wets, into the evolutionary programming paradigm. The proposed hybrid optimization approach(More)
Nonlinear data-driven dimensionality reduction techniques have recently gained popularity due to the emergence of high dimensional data sets. The algorithmic complexity and storage requirements of these techniques, however, can make them prohibitive in resource-limited applications. It is therefore beneficial to reduce the number of exemplar samples(More)
In image classification tasks, the image is rarely represented as only a collection of raw pixels. Myriad alternative representations, from Gaussian kernels to bags-of-words to layers of a convolutional neural network, have been proposed both to decrease the dimensionality of the task and, more importantly, to move into a space which better facilitates(More)
This work investigates a combined stochastic and deterministic optimization approach for multivariate mixture density estimation. Mixture probability density models are selected and optimized by combining the optimization characteristics of a multiagent stochastic optimization algorithm based on evolutionary programming and the expectation-maximization(More)
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