Learning the unified Kernel machines for classification

• Main Authors: HOI, Steven C. H., LYU, Michael R., CHANG, Edward Y.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Classification; Kernel Machines; Spectral Kernel Learning; Supervised Learning; Semi-Supervised Learning; Unsupervised Kernel Design; Kernel Logistic Regressions; Active Learning; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/2388; https://ink.library.smu.edu.sg/context/sis_research/article/3388/viewcontent/KDD06UKM.pdf
• Institution: Singapore Management University

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, semi-supervis...