Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
This work addresses a foundational challenge in online learning for machine learning practitioners, offering a novel solution with proven improvements.
The paper tackles the problem of improving mistake bounds in budgeted online kernel learning by proposing Ahpatron, which resolves an open problem and achieves tighter theoretical bounds while outperforming state-of-the-art algorithms in experiments.
In this paper, we study the mistake bound of online kernel learning on a budget. We propose a new budgeted online kernel learning model, called Ahpatron, which significantly improves the mistake bound of previous work and resolves the open problem posed by Dekel, Shalev-Shwartz, and Singer (2005). We first present an aggressive variant of Perceptron, named AVP, a model without budget, which uses an active updating rule. Then we design a new budget maintenance mechanism, which removes a half of examples,and projects the removed examples onto a hypothesis space spanned by the remaining examples. Ahpatron adopts the above mechanism to approximate AVP. Theoretical analyses prove that Ahpatron has tighter mistake bounds, and experimental results show that Ahpatron outperforms the state-of-the-art algorithms on the same or a smaller budget.