contributor author | Richard J. Malak | |
contributor author | Christiaan J. J. Paredis | |
date accessioned | 2017-05-09T00:39:30Z | |
date available | 2017-05-09T00:39:30Z | |
date copyright | October, 2010 | |
date issued | 2010 | |
identifier issn | 1050-0472 | |
identifier other | JMDEDB-27932#101001_1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/144141 | |
description abstract | Predictive modeling can be a valuable tool for systems designers, allowing them to capture and reuse knowledge from a set of observed data related to their system. An important challenge associated with predictive modeling is that of describing the domain over which model predictions are valid. This is necessary to avoid extrapolating beyond the original data, particularly when designers use predictive models in concert with optimizers or other computational routines that search a model’s input space automatically. The general problem of domain description is complicated by the characteristics of observational data sets, which can contain small numbers of samples, can have nonlinear associations among the variables, can be nonconvex, and can occur in disjoint clusters. Support vector machine (SVM) techniques, developed originally in the machine learning community, offer a solution to this problem. This paper is a description of a kernel-based SVM approach that yields a formal mathematical description of the valid input domain of a predictive model. The approach also provides for cluster analysis, which can lead to improved model accuracy through the decomposition of a data set into multiple subsets that designers can model independently. This paper includes a mathematical presentation of kernel-based SVM methods, an explanation of the procedure for applying the approach to predictive modeling problems, and illustrative examples for applying and using the approach in systems design. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Using Support Vector Machines to Formalize the Valid Input Domain of Predictive Models in Systems Design Problems | |
type | Journal Paper | |
journal volume | 132 | |
journal issue | 10 | |
journal title | Journal of Mechanical Design | |
identifier doi | 10.1115/1.4002151 | |
journal fristpage | 101001 | |
identifier eissn | 1528-9001 | |
keywords | Machinery | |
keywords | Engines | |
keywords | Algorithms | |
keywords | Design | |
keywords | Modeling | |
keywords | Optimization | |
keywords | Support vector machines AND Fittings | |
tree | Journal of Mechanical Design:;2010:;volume( 132 ):;issue: 010 | |
contenttype | Fulltext | |