کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532296 869931 2013 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multitask multiclass support vector machines: Model and experiments
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Multitask multiclass support vector machines: Model and experiments
چکیده انگلیسی

Multitask learning or learning multiple related tasks simultaneously has shown a better performance than learning these tasks independently. Most approaches to multitask multiclass problems decompose them into multiple multitask binary problems, and thus cannot effectively capture inherent correlations between classes. Although very elegant, traditional multitask support vector machines are restricted by the fact that different learning tasks have to share the same set of classes. In this paper, we present an approach to multitask multiclass support vector machines based on the minimization of regularization functionals. We cast multitask multiclass problems into a constrained optimization problem with a quadratic objective function. Therefore, our approach can learn multitask multiclass problems directly and effectively. This approach can learn in two different scenarios: label-compatible and label-incompatible multitask learning. We can easily generalize the linear multitask learning method to the non-linear case using kernels. A number of experiments, including comparisons with other multitask learning methods, indicate that our approach for multitask multiclass problems is very encouraging.


► This method learns multitask multiclass problems directly and effectively.
► It can learn in label-compatible and label-incompatible MTL scenarios.
► It generalizes the linear MTL to the non-linear multitask kernel learning.
► Experimental results indicate that our method is very encouraging.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 46, Issue 3, March 2013, Pages 914–924
نویسندگان
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