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We have recently introduced new generative semi supervised mixtures with more fine-grained class label generation mechanisms than previous methods. Our models combine advantages of semi supervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. Our...
In the active learning challenge, we aim to improve the area under the learning curve (ALC), the global score in the challenge, by optimizing the classification methods and feature selection methods, and most importantly by refining the querying algorithm to select the most informative instances in the early iteration of active learning. For six different datasets in the development phase, we applied...
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort. In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active...
Nowadays, various learning technologies are required on uncertain data. As an important pre-processing step in data mining, feature selection needs to consider this vagueness or uncertainty. In this paper, we propose a novel algorithm to evaluate the correlation between features and uncertain class labels on the basis of Hilbert-Schmidt Independence Criterion. Consequently, the features can be ranked...
Machine learning algorithms are known to degrade in performance when facing with many features that are not necessary in the field of artificial intelligence and pattern recognition. Rough set theory is a new effective tool in dealing with vagueness and uncertainty information. Attribute reduction is one of the most important concepts in rough set theory and application research. Once it gets the...
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