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- W2546541952 abstract "In this paper, we present that Learning by propagability is a novel feature extraction framework. The entire leaning methodology is based on the data labels and optimality of feature representation that can create a harmonic system. Here data labels are invariant regarding the propagation on the similarity graph constructed based on the optimal feature representation. Using this idea, we can perform classification. This approach deals with multiclass classification problems. Dealing with uncertain labels is the crucial problem in today’s world. Here, we have tried to explain theory of belief functions which is used to treat uncertain labels. Applying two or more learning rules, we can implement the classification procedure for uncertain labels data sets." @default.
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- W2546541952 date "2014-12-01" @default.
- W2546541952 modified "2023-09-23" @default.
- W2546541952 title "A Literature Survey on Learning a Propagable Graph for Classification under the Scenario with Uncertain Labels" @default.
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