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      Modeling heating and cooling loads by artificial intelligence for energy-efficient building design

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      Energy and Buildings
      Elsevier BV

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          Practical selection of SVM parameters and noise estimation for SVM regression.

          We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, epsilon-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low- and high-dimensional regression problems. Further, we point out the importance of Vapnik's epsilon-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of epsilon-values) with regression using 'least-modulus' loss (epsilon=0) and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise.
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            EnergyPlus: creating a new-generation building energy simulation program

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              An Exploratory Technique for Investigating Large Quantities of Categorical Data

              G. Kass (1980)
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                Author and article information

                Journal
                Energy and Buildings
                Energy and Buildings
                Elsevier BV
                03787788
                October 2014
                October 2014
                : 82
                : 437-446
                Article
                10.1016/j.enbuild.2014.07.036
                60407165-1590-4f8d-bbfa-4b30101b262a
                © 2014

                https://www.elsevier.com/tdm/userlicense/1.0/


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