کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10362202 870652 2005 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Exploratory basis pursuit classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Exploratory basis pursuit classification
چکیده انگلیسی
Feature selection is a fundamental process in many classifier design problems. However, it is NP-complete and approximate approaches often require requires extensive exploration and evaluation. This paper describes a novel approach that represents feature selection as a continuous regularization problem which has a single, global minimum, where the model's complexity is measured using a 1-norm on the parameter vector. A new exploratory design process is also described that allows the designer to efficiently construct the complete locus of sparse, kernel-based classifiers. It allows the designer to investigate the optimal parameters' trajectories as the regularization parameter is altered and look for effects, such as Simpson's paradox, that occur in many multivariate data analysis problems. The approach is demonstrated on the well-known Australian Credit data set.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 26, Issue 12, September 2005, Pages 1907-1915
نویسندگان
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