Abstract Kernel ridge regression (KRR) is an efficient method for regression task. However, KRR has a deficiency in finding appropriate type of kernel functions and their parameters. To overcome this shortcoming, a novel kernel ensemble framework is developed. In this ensemble framework, each kernel regressor is associated with a weight that can be adaptively determined according to its contribution to the regression result. By this way, more appropriate kernels and more accurate parameters can be learned directly from data without any manual intervention, which results in better performance in regression. In addition, to overcome the influence of existing outliers, the regressor loss is modeled as a sparse signal, thus a Sparse Loss induced Kernel Ensemble Regression (SLiKER) method is obtained. Experimental results on several UCI regression and computer vision datasets show that our proposed approach obtains best regression and classification performances among the state-of-art comparative methods.

中文翻译：

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SLiKER：稀疏损失引起的核集成回归

摘要 核岭回归（KRR）是一种有效的回归任务方法。然而，KRR 在寻找合适类型的核函数及其参数方面存在不足。为了克服这个缺点，开发了一种新颖的内核集成框架。在这个集成框架中，每个核回归器都与一个权重相关联，该权重可以根据其对回归结果的贡献自适应地确定。通过这种方式，可以直接从数据中学习到更合适的内核和更准确的参数，而无需任何人工干预，从而在回归中获得更好的性能。此外，为了克服现有异常值的影响，将回归器损失建模为稀疏信号，从而获得稀疏损失诱导核集成回归（SLiKER）方法。