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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Zhou, ZH. (2009). A Framework for Machine Learning with Ambiguous Objects. In: Liu, J., Wu, J., Yao, Y., Nishida, T. (eds) Active Media Technology. AMT 2009. Lecture Notes in Computer Science, vol 5820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04875-3_6
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DOI: https://doi.org/10.1007/978-3-642-04875-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04874-6
Online ISBN: 978-3-642-04875-3
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