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- W1653065281 abstract "Machine learning tries to improve the performance of the system automatically by learning from experiences, e.g., objects or events given to the system as training samples. Generally, each object is represented by an instance (or feature vector) and is associated with a class label indicating the semantic meaning of that object. For ambiguous objects which have multiple semantic meanings, traditional machine learning frameworks may be less powerful. This talk will introduce a new framework for machine learning with ambiguous objects." @default.
- W1653065281 created "2016-06-24" @default.
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- W1653065281 date "2009-01-01" @default.
- W1653065281 modified "2023-09-25" @default.
- W1653065281 title "A Framework for Machine Learning with Ambiguous Objects" @default.
- W1653065281 doi "https://doi.org/10.1007/978-3-642-04875-3_6" @default.
- W1653065281 hasPublicationYear "2009" @default.
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