The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Invariant kernel functions for pattern analysis and machine learning
Machine Learning
Classification of microorganisms via Raman spectroscopy using Gaussian processes
Proceedings of the 32nd DAGM conference on Pattern recognition
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For the classification of biological samples based on Raman spectra, a robust classifier is necessary. This requirement is met by using Support Vector Machines (SVMs) enhanced by incorporating a-priori knowledge about pattern variations. In the described approach transformation knowledge is included directly into the classification process by using regularized tangent distance kernels. This approach replaces the standard Euclidean distance in the kernel function by the distance of the linear approximation (tangent spaces) of known transformation manifolds. These transformations represent first a global scaling of the spectral values referring to intensity variations, and second a baseline shift by Lagrange polynomials. Experiments are carried out and reported in this paper. The results show, that incorporating a-priori knowledge by tangent distances improves the classification rates substantially, while a lossy baseline correction becomes superfluous.