Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Using transformation knowledge for the classification of Raman spectra of biological samples
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Learning kernels from indefinite similarities
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Automatic categorization of microorganisms is a complex task which requires advanced techniques to achieve accurate performance. In this paper, we aim at identifying microorganisms based on Raman spectroscopy. Empirical studies over the last years show that powerful machine learning methods such as Support Vector Machines (SVMs) are suitable for this task. Our work focuses on the Gaussian process (GP) classifier which is new to this field, provides fully probabilistic outputs and allows for efficient hyperparameter optimization. We also investigate the incorporation of prior knowledge regarding possible signal variations where known concepts from invariant kernel theory are transferred to the GP framework. In order to validate the suitability of the GP classifier, a comparison with state-of-the-art learners is conducted on a large-scale Raman spectra dataset, showing that the GP classifier significantly outperforms all other tested classifiers including SVM. Our results further show that incorporating prior knowledge leads to a significant performance gain when small amounts of training data are used.