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We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to lead to good generalization ability. We show that these two properties are fundamentally at odds with each other: A sparse algorithm cannot be stable and vice versa. Thus, one has to trade off sparsity and stability in designing a learning algorithm. In particular, our general...
We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. Our formulation is based on a softer version of robust optimization called comprehensive robustness. We show that this formulation is equivalent to regularization by any arbitrary convex regularizer. We explain how the connection...
In recent research, distinguishing photographs and graphics was regarded as a typical two-class pattern classification task. In practice, photographs pattern model can be established easily, while it is hard to gain a perfect graphics pattern model. In this paper, we develop a novel approach based on one-class support vector machine (OCSVM) to distinguish photographs and graphics. In this approach,...
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