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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...
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...
A completely new type of fuzzy logic system will be developed from the existing fuzzy structure and applied to modeling and control of complex processes under incomplete dynamics in the manufacturing industry. Using a unique three-dimensional membership function (fuzz grade, time and probability), the probabilistic processing features can be added into the existing fuzzy configuration to construct...
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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