Home
Research
Publications
Resume
Courses
Links
Astronomy
Travel
|
Semi-Supervised Learning
Can unlabeled data be used to train a
classifier? Traditional classifiers need labeled data (feature /
label pairs) to train. Labeled instances however are often
difficult, expensive, or time consuming to obtain, as they require the
efforts of experienced human annotators. Meanwhile unlabeled data
may be relatively easy to collect, but there has been few ways to use
them. Semi-supervised learning addresses this problem by using
large amount of unlabeled data, together with the labeled data, to
build better classifiers. Because semi-supervised learning
requires less human effort and gives higher accuracy, it is of
great interest both in theory and in practice.
|
Statistical Language Modeling
|
Multimodal User Interfaces
|
|