Biomedical subjects

A Wendemuth

Publications and source records attributed to A Wendemuth.

3 recordsLinked to original sources

PubMed2006

Kernel least-squares models using updates of the pseudoinverse.

Sparse nonlinear classification and regression models in reproducing kernel Hilbert spaces (RKHSs) are considered. The use of Mercer kernels and the square loss function gives rise to an overdetermined linear least-squares problem in the corresponding RKHS. When we apply a greedy forward selection scheme, the least-squares problem may be solved by an order-recursive update of the pseudoinverse in each iteration step. The computational time is linear with respect to the number of the selected training samples.

Computer Simulation↗

PubMed1994

Storage capacity bounds in multilayer neural networks.

General analytic expressions are given for the lower and upper storage capacity bounds of multilayer neural networks which have variable weights between input layer and first hidden layer, and a fixed output function implemented between first hidden and output layer. The special cases of committee and parity machines as well as the limiting cases of networks with minimum and maximum storage capacities are discussed. The results are compared with replica calculations and simulations. An explanation is given as to why the latter have a storage capacity just slightly above the lower limit and how this can be improved.

Algorithms↗

PubMed1993

Fast learning of biased patterns in neural networks.

Usual neural network gradient descent training algorithms require training times of the same order as the number of neurons N if the patterns are biased. In this paper, modified algorithms are presented which require training times equal to those in unbiased cases which are of order 1. Exact convergence proofs are given. Gain parameters which produce minimal learning times in large networks are computed by replica methods. It is demonstrated how these modified algorithms are applied in order to produce four types of solutions to the learning problem: 1. A solution with all internal fields equal to the desired output, 2. The Adaline (or pseudo-inverse) solution, 3. The perceptron of optimal stability without threshold and 4. The perceptron of optimal stability with threshold.

Algorithms↗