On Multi-View Active Learning and the Combination with Semi-Supervised Learning
published: Aug. 1, 2008, recorded: July 2008, views: 5640
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Multi-view learning has become a hot topic during the past few years. In this paper, we first characterize the sample complexity of multi-view active learning. Under the α-expansion assumption, we get an exponential improvement in the sample complexity from usual Õ(1/ε) to Õ(log 1/ε), requiring neither strong assumption on data distribution such as the data is distributed uniformly over the unit sphere in ℜd nor strong assumption on hypothesis class such as linear separators through the origin. We also give an upper bound of the error rate when the α-expansion assumption does not hold. Then, we analyze the combination of multi-view active learning and semi-supervised learning and get a further improvement in the sample complexity. Finally, we study the empirical behavior of the two paradigms, which verifies that the combination of multi-view active learning and semi-supervised learning is efficient.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !