Multi-class classification algorithms are very widely used, but we argue that they are not always ideal from a theoretical perspective, because they assume all classes are characte...
The past decade has seen extensive research on audio classification and segmentation algorithms. However, the effect of background noise on the performance of classification has n...
: We propose a new nonparametric family of oscillation heuristics for improving linear classifiers in the two-group discriminant problem. The heuristics are motivated by the intuit...
Background: Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimentin...
Lior Shamir, Nikita Orlov, D. Mark Eckley, Tomasz ...
A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data...