Section Days abstract book 2010.indd - RUB Research School ...

LS_17
HYDROACOUSTIC SIGNAL CLASSIFICATION
USING KERNEL FUNCTIONS FOR VARIABLE
FEATURE SETS
Matthias Tuma, Christian Igel
Institut für Neuroinformatik, Ruhr-Universität-Bochum, 44780 Bochum, Germany
Research School, Life Sciences Section
e-mail: matthias.tuma@rub.de
Large-scale geophysical monitoring systems raise the need for real-time feature extraction
and signal classification. We study support vector machine (SVM) classification of
hydroacoustic signals recorded by the Comprehensive Nuclear-Test-Ban Treaty's verification
network. Due to constraints in the early signal processing most samples have different feature
sets with values missing not at random. We propose kernel functions explicitly incorporating
Boolean representations of the missingness patterns through dedicated sub-kernels. Model
selection using gradient ascent on the kernel-target alignment and different extensions of
SVMs for multiple classes were employed. While too complex kernel functions led to
overfitting, SVMs with less flexible kernels outperformed baseline methods.