Condition: very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages.
Language: English
Published by Omniscriptum Apr 2026, 2026
ISBN 10: 6134609153 ISBN 13: 9786134609159
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch.
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Projection Pursuit Regression | Statistics, Statistical Model, Explanatory Variable, Gauss-Newton | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786134609159 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Omniscriptum Apr 2026, 2026
ISBN 10: 6134609153 ISBN 13: 9786134609159
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. In statisticsprojection pursuit regression (PPR) is a statistical model developed byJerome H. Friedman and Werner Stuetzle which is an extension of additivemodels. This model adapts the additive models in that it first projectsthe data matrix of explanatory variables in the optimal direction beforeapplying smoothing functions to these explanatory variables. The modelconsists of linear combinations of non-linear transformations of linearcombinations of explanatory variables. Both projection pursuitregression and neural networks models project the input vector onto aone-dimensional hyperplane and then apply a nonlinear transformation ofthe input variables that are then added in a linear fashion. Thus bothfollow the same steps to overcome the curse of dimensionality. The maindifference is that the functions fj being fitted in PPR can be differentfor each combination of input variables and are estimated one at a timeand then updated with the weights, whereas is NN these are all specifiedupfront and estimated simultaneously.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.