Non-metric proximity measures got wide interest in various domains such as life sciences, robotics and image processing. The majority of learning algorithms for these data are focusing on classification problems. Here we derive a regression algorithm for indefinite data representations based on the support vector machine. The approach avoids heuristic eigen spectrum modifications or costly proxy matrix approximations, as used in general. We evaluate the method on a number of benchmark data using an indefinite measure.
CITATION STYLE
Schleif, F. M. (2017). Indefinite support vector regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10614 LNCS, pp. 313–321). Springer Verlag. https://doi.org/10.1007/978-3-319-68612-7_36
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