کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
412962 679708 2009 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An experimental study on methods for the selection of basis functions in regression
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
An experimental study on methods for the selection of basis functions in regression
چکیده انگلیسی

A comparative study is carried out in the problem of selecting a subset of basis functions in regression tasks. The emphasis is put on practical requirements, such as the sparsity of the solution or the computational effort. A distinction is made according to the implicit or explicit nature of the selection process. In explicit selection methods the basis functions are selected from a set of candidates with a search process. In implicit methods a model with all the basis functions is considered and the model parameters are computed in such a way that several of them become zero. The former methods have the advantage that both the sparsity and the computational effort can be controlled. We build on earlier work on Bayesian interpolation to design efficient methods for explicit selection guided by model evidence, since there is strong indication that the evidence prefers simple models that generalize fairly well. Our experimental results indicate that very similar results between implicit and explicit methods can be obtained regarding generalization performance. However, they make use of different numbers of basis functions and are obtained at very different computational costs. It is also reported that the models with the highest evidence are not necessarily those with the best generalization performance.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 72, Issues 13–15, August 2009, Pages 2952–2963
نویسندگان
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