Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
On a theory of learning with similarity functions
ICML '06 Proceedings of the 23rd international conference on Machine learning
A discriminative framework for clustering via similarity functions
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Theory and algorithm for learning with dissimilarity functions
Neural Computation
Expert Systems with Applications: An International Journal
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Recently, Balcan and Blum [1] suggested a theory of learning based on general similarity functions, instead of positive semidefinite kernels. We study the gap between the learning guarantees based on kernel-based learning, and those that can be obtained by using the kernel as a similarity function, which was left open by Balcan and Blum. We provide a significantly improved bound on how good a kernel function is when used as a similarity function, and extend the result also to the more practically relevant hinge-loss rather then zero-one-error-rate. Furthermore, we show that this bound is tight, and hence establish that there is in-fact a real gap between the traditional kernel-based notion of margin and the newer similarity-based notion.