Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a cost-sensitive perceptron learning rule for non-separable classes, that can be extended to multi-modal classes (DIPOL) and present a natural cost-sensitive extension of the support vector machine (SVM). © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Geibel, P., Brefeld, U., & Wysotzki, F. (2003). Learning linear classifiers sensitive to example dependent and noisy costs. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2810, 167–178. https://doi.org/10.1007/978-3-540-45231-7_16
Mendeley helps you to discover research relevant for your work.