uncovering Shared Structures in Multiclass Classification: Difference between revisions
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In their paper ''Uncovering Shared Structures in Multiclass Classification'', the authors Amit ''et al.'' wrote about how hidden structure can be utilized to improve the accuracy in multiclass classification. The uncovering of such hidden structure was accomplished | In their paper ''Uncovering Shared Structures in Multiclass Classification'', the authors Amit ''et al.'' wrote about how hidden structure can be utilized to improve the accuracy in multiclass classification. The uncovering of such hidden structure was accomplished | ||
by a mechanism that learns the underlying characteristics that are shared between the target classes. The benefits of finding such common characteristics was demonstrated in the context of large margin multiclass linear classifiers. | by a mechanism that learns the underlying characteristics that are shared between the target classes. The benefits of finding such common characteristics was demonstrated in the context of large margin multiclass linear classifiers. | ||
Accurate classification of an instance when there is a large number of target classes is a major challenge in many areas such as object recognition, face identification, textual topic classification, and phoneme recognition. Usually, when there is a large number of classes, the classes are strongly related to each other and have some underlying common characteristics. |
Revision as of 10:21, 22 November 2010
Introduction
In their paper Uncovering Shared Structures in Multiclass Classification, the authors Amit et al. wrote about how hidden structure can be utilized to improve the accuracy in multiclass classification. The uncovering of such hidden structure was accomplished by a mechanism that learns the underlying characteristics that are shared between the target classes. The benefits of finding such common characteristics was demonstrated in the context of large margin multiclass linear classifiers.
Accurate classification of an instance when there is a large number of target classes is a major challenge in many areas such as object recognition, face identification, textual topic classification, and phoneme recognition. Usually, when there is a large number of classes, the classes are strongly related to each other and have some underlying common characteristics.