کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9653359 | 679045 | 2005 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Loss functions to combine learning and decision in multiclass problems
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The design of structures and algorithms for non-MAP multiclass decision problems is discussed in this paper. We propose a parametric family of loss functions that provides accurate estimates for the posterior class probabilities near the decision regions. Moreover, we discuss learning algorithms based on the stochastic gradient minimization of these loss functions. We show that these algorithms behave like sample selectors: samples near the decision regions are the most relevant during learning. Moreover, it is shown that these loss functions can be seen as an alternative to support vector machines (SVM) classifiers for low-dimensional feature spaces. Experimental results on some real data sets are also provided to show the effectiveness of this approach versus the classical cross entropy (based on a global posterior probability estimation).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 69, Issues 1â3, December 2005, Pages 3-17
Journal: Neurocomputing - Volume 69, Issues 1â3, December 2005, Pages 3-17
نویسندگان
Alicia Guerrero-Curieses, RocÃo Alaiz-RodrÃguez, Jesús Cid-Sueiro,