Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
Research Collection School Of Computing and Information Systems
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-supervised 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 …
Guided Interactive Machine Learning, Aaron J. Pace
Guided Interactive Machine Learning, Aaron J. Pace
Theses and Dissertations
This thesis describes a combination of two current areas of research: the Crayons image classifier system and active learning. Currently Crayons provides no guidance to the user in what pixels should be labeled or when the task is complete. This work focuses on two main areas: 1) active learning for user guidance, and 2) accuracy estimation as a measure of completion. First, I provide a study through simulation and user experiments of seven active learning techniques as they relate to Crayons. Three of these techniques were specifically designed for use in Crayons. These three perform comparably to the others and …