We previously reported on the existence of a family of lipoprotein genes, designated 2.9 lipoprotein genes, encoded in at least seven versions on the circular (supercoiled) cp32 and cp18 plasmids of Borrelia burgdorferi 297. A distinguishing feature of the 2.9 lipoproteins were highly similar signal sequences but variable mature polypeptides that segregated into two antigenic classes. Further s...

We define a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma filters (as proposed by de Vries and Principe (de Vries and Principe, 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We find that both the inclusion of gamma filters in all layer...

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a ...

We deene a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma lters (as proposed by de Vries and Principe (de Vries & Principe 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We nd that both the inclusion of gamma lters in all layers, and the...

In this paper, an empirical comparison of two multilayer perceptron (MLP)-based techniques for keyword speech recognition (wordspotting) is described. The techniques are the predictive neural model (PNM)-based wordspotting, in which the MLP is applied as a speech pattern predictor to compute a local distance between the acoustic vector and the phone model, and the hybrid HMM/MLP-based wordspott...

In this paper, we propose a way to apply Multi Layer Perceptron (MLP) to Functional Data Analysis. We introduce a computation model for functional input data and we show that this model is a well behaving extension of MLP: we show that the proposed model has the universal approximation property. Moreover, parameter estimation for this model is consistent. As a conclusion, we demonstrate functio...