|Title||Multicategory angle-based large-margin classification.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Zhang, Chong, and Yufeng Liu|
|Date Published||2014 Sep|
Large-margin classifiers are popular methods for classification. Among existing simultaneous multicategory large-margin classifiers, a common approach is to learn different functions for a -class problem with a sum-to-zero constraint. Such a formulation can be inefficient. We propose a new multicategory angle-based large-margin classification framework. The proposed angle-based classifiers consider a simplex-based prediction rule without the sum-to-zero constraint, and enjoy more efficient computation. Many binary large-margin classifiers can be naturally generalized for multicategory problems through the angle-based framework. Theoretical and numerical studies demonstrate the usefulness of the angle-based methods.
|Original Publication||Multicategory angle-based large-margin classification.|
|PubMed Central ID||PMC4629508|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Multicategory angle-based large-margin classification.