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In addition, a probabilistic generative model is presented for estimating the noise distribution. This enables us to determine the noise distribution parametrically from a single noisy input, using the distribution of the noise-free constituent of noisy input estimated from the training data set as a prior. Experiments conducted using artificial and real data sets show that the proposed method suppresses the overfitting of the regression function for noisy inputs and the root mean squared errors (RMSEs) of the predictions are smaller compared with those of an existing method.<\/jats:p>","DOI":"10.1142\/s2424922x20500047","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T06:07:46Z","timestamp":1594188466000},"page":"2050004","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Kernel Regression for Noisy Inputs Based on Nadaraya\u2013Watson Estimator Constructed from Noiseless Training Data"],"prefix":"10.1142","volume":"12","author":[{"given":"Ryo","family":"Hanafusa","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Kwansei Gakuin University, 2\u20131, Gakuen, Sanda\u2013shi, Hyogo 669 1337, Japan"}]},{"given":"Takeshi","family":"Okadome","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Kwansei Gakuin University, 2\u20131, Gakuen, Sanda\u2013shi, Hyogo 669 1337, Japan"}]}],"member":"219","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"S2424922X20500047BIB001","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition.","author":"Bishop C. 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