Smoothing parameter selection in semi-supervised learning

Instance Selection in Semi-supervised Learning

Semi-supervised learning methods utilize abundant unlabeled data to help to learn a better classifier when the number of labeled instances is very small. A common method is to select and label unlabeled instances that the current classifier has high classification confidence to enlarge the labeled training set and then to update the classifier, which is widely used in two paradigms of semi-supe...

Data Selection for Semi-Supervised Learning

The real challenge in pattern recognition task and machine learning process is to train a discriminator using labeled data and use it to distinguish between future data as accurate as possible. However, most of the problems in the real world have numerous data, which labeling them is a cumbersome or even an impossible matter. Semi-supervised learning is one approach to overcome these types of p...

Hyper-parameter Learning for Graph Based Semi-supervised Learning Algorithms

Efficient Computation and Model Selection in Semi-Supervised Learning

Traditional learning algorithm uses only labeled data for training. However, labeled examples are often difficult or time consuming to obtain since they require substantial labeling efforts from humans. On the other hand, unlabeled data are often relatively easy to collect. Semi-supervised learning addresses this problem by using large quantities of unlabeled data with the labeled data to build...

Data Selection In Semi-Supervised Learning For Name Tagging

We present two semi-supervised learning techniques to improve a state-of-the-art multi-lingual name tagger. For English and Chinese, the overall system obtains 1.7% 2.1% improvement in F-measure, representing a 13.5% 17.4% relative reduction in the spurious, missing, and incorrect tags. We also conclude that simply relying upon large corpora is not in itself sufficient: we must pay attention to...