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This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value to each data example in the training data, which measures the strength of the corresponding class label. Our proposed method works in two steps. Firstly, we generate a pseudo training...
The goal of active learning is to minimize the amount of labeled data required for machine learning. Some methods have focused on exploiting the samples with high uncertainty, but those methods fail in getting a representative set of the data samples. Other methods try to explore the representative samples by utilizing the prior distribution of the dataset. However, they are often computationally...
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods for multi-class problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we introduce a probabilistic variant of the K-nearest neighbor method for classification that can be seamlessly...
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...
One of the main problems in information retrieval is ranking documents according to their relevance to userspsila queries. Learning to rank is considered as a promising approach for addressing the issue. However, like many other supervised approaches, one of the main problems with learning to rank is the lack of labeled data, as well as labeling instances to create a rank model is time-consuming and...
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