IEEE Transactions on Semiconductor Manufacturing, 19(4):475-486, February 2006 (article)
Fluctuations are inherent to any fabrication process.
Integrated circuits and micro-electro-mechanical systems are
particularly affected by these variations, and due to high quality
requirements the effect on the devices performance has to be
understood quantitatively. In recent years it has become possible
to model the performance of such complex systems on the basis
of design specifications, and model-based Sensitivity Analysis
has made its way into industrial engineering. We show how an
efficient Bayesian approach, using a Gaussian process prior, can
replace the commonly used brute-force Monte Carlo scheme,
making it possible to apply the analysis to computationally costly
models. We introduce a number of global, statistically justified
sensitivity measures for design analysis and optimization. Two
models of integrated systems serve us as case studies to introduce
the analysis and to assess its convergence properties. We show
that the Bayesian Monte Carlo scheme can save costly simulation
runs and can ensure a reliable accuracy of the analysis.
In Advances in Neural Information Processing Systems 15, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)
We investigate data based procedures for selecting the kernel when learning with Support Vector Machines. We provide generalization error bounds by estimating the Rademacher complexities of the corresponding function classes. In particular we obtain a complexity bound for function classes induced by kernels with given eigenvectors, i.e., we allow to vary the spectrum and keep the eigenvectors fix. This bound is only a logarithmic factor bigger than the complexity of the function class induced by a single kernel. However, optimizing the margin over such classes leads to overfitting. We thus propose a suitable way of constraining the class. We use an efficient algorithm to solve the resulting optimization problem, present preliminary experimental results, and compare them
to an alignment-based approach.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems