Learning the unified kernel machines for classification

Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel Machines (UKM) from both labeled and unlabeled data. Our proposed framework integrates supervised learning, semisupervised kernel learning, and active learning in a unified solution. In the suggested framework, we particularly focus our attention on designing a new semi-supervised kernel learning method, i.e., Spectral Kernel Learning (SKL), which is built on the principles of kernel target alignment and unsupervised kernel design. Our algorithm is related to an equivalent quadratic programming problem that can be efficiently solved. Empirical results have shown that our method is more effective and robust to learn the semisupervised kernels than traditional approaches. Based on the framework, we present a specific paradigm of unified kernel machines with respect to Kernel Logistic Regresions (KLR), i.e., Uni...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
• Keywords: Data Mining, KDD 2006, Kernel Learning Method, Unified Kernel Machines, Unsupervised Kernel Design