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Titlebook: Nonlinear Estimation and Classification; David D. Denison,Mark H. Hansen,Bin Yu Book 2003 Springer Science+Business Media New York 2003 AN

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發(fā)表于 2025-3-21 16:51:04 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Nonlinear Estimation and Classification
編輯David D. Denison,Mark H. Hansen,Bin Yu
視頻videohttp://file.papertrans.cn/668/667481/667481.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Nonlinear Estimation and Classification;  David D. Denison,Mark H. Hansen,Bin Yu Book 2003 Springer Science+Business Media New York 2003 AN
描述Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.
出版日期Book 2003
關(guān)鍵詞ANOVA; Estimator; data analysis; probability; statistical inference; statistics; time series
版次1
doihttps://doi.org/10.1007/978-0-387-21579-2
isbn_softcover978-0-387-95471-4
isbn_ebook978-0-387-21579-2Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer Science+Business Media New York 2003
The information of publication is updating

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Regression and Classification with Regularization3] as the problem of approximating a multivariate function from sparse data.. The function can be real valued as in regression or binary valued as in classification. The problem of approximating a function from sparse data is ill-posed and a classical solution is regularization theory [19].
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David D. Denison,Mark H. Hansen,Bin YuIncludes supplementary material:
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978-0-387-95471-4Springer Science+Business Media New York 2003
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