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UC Denver Operations Research Readings and Seminars
Map and driving instructions (and parking information) . Wednesday, September 5, 2007
Overflow probabilities in tandem queues ABSTRACT:
Wednesday, September 19, 2007
Tutorial seminar: Semidefinite programming and 0.878 approximation-guarantee on the max-cut problem by Goemans and Williamson ABSTRACT:
Friday, October 19, 2007
MULTI-VALUED AND UNIVERSAL BINARY NEURONS: NEW SOLUTIONS IN NEURAL NETWORKS ABSTRACT: The MVN inputs and output are lying on the unit circle, and its activation
function maps the complex plane into the unit circle. The MVN learning
algorithm is reduced to the movement along the unit circle. The MVN learning
algorithm is based on a simple linear error correction rule and it is
derivative-free. The functionality of a single MVN is much higher than the
functionality of other traditionally used neurons. It is very interesting that
the states of "maximum excitation" and "maximum inhibition" of MVN coincide
with each other, while a "medium" state is equidistant from them. Thus, the
neuron can change its state from excitatory to inhibitory and vice versa either
passing all intermediate states or in the shortest possible way. This MVN
paradigm can be very interesting for the simulations of the natural neurons.
The most impressive and recently developed application of MVN is a multilayer
feedforward neural network based on multi-valued neurons (MLMVN). The
backpropagation learning algorithm for the MLMVN is derivative-free. Being
similar to the classical backpropagation algorithm, it has important
distinctions that make it more stable and less heuristic. These distinctions
are derivative-free learning and a self adaptation of the learning rate for the
hidden neurons. The MLMVN is a powerful tool for solving multiple-class
classification, recognition and prediction problems. It outperforms a classical
backpropagation network, different kernel-based networks including SVM in terms
of complexity and classification/prediction rate solving a number of popular
benchmark problems. Some successful real world applications of the MLMVN have
been recently developed (blur identification for solving the image restoration
problem and classification of microarray gene expression data). The MLMVN with
just one hidden layer containing 4 neurons and one output neuron can be used as
a universal generator of the genetic code.
The activation function of UBN similarly to the MVN activation functions is
also a function of the argument of the weighted sum: a complex plane is
separated onto equal sectors, where the activation function is equal to 1 or -1
depending on the sector's parity. This makes possible learning of the
nonlinearly separable Boolean functions on a single neuron. Thus, the
functionality of UBN is incompatibly higher than the functionality of the
traditional single perceptron. For example, the classical nonlinearly separable
problems XOR and Parity N (for arbitrary N) can be easily solved using a single
UBN. The UBN learning algorithm similarly to the MVN learning algorithm is also
reduced to the movement along the unit circle, and it is based on the linear
error correcting learning rule.
The presentation will be illustrated by a number of impressive examples of
using MVN, MLMVN and UBN for solving different problems.
Wednesday, October 31, 2007
Real Time Integer Programming to Improve Ambulance Response Time at San Francisco Fire Department ABSTRACT:
Wednesday, December 5, 2007
Simulating Lung Cancer Risk from Conventional and Modified Cigarettes ABSTRACT:
Join our mailing list by sending your name and e-mail address to Harvey.Greenberg@cudenver.edu.
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