title: Support vector machines for predictive modeling in heterogeneous catalysis: a comprehensive introduction and overfitting investigation based on two real applications.
abstract: This works provides an introduction to support vector machines (SVMs) for predictive modeling in heterogeneous catalysis, describing step by step the methodology with a highlighting of the points which make such technique an attractive approach. We first investigate linear SVMs, working in detail through a simple example based on experimental data derived from a study aiming at optimizing olefin epoxidation catalysts applying high-throughput experimentation. This case study has been chosen to underline SVM features in a visual manner because of the few catalytic variables investigated. It is shown how SVMs transform original data into another representation space of higher dimensionality. The concepts of Vapnik-Chervonenkis dimension and structural risk minimization are introduced. The SVM methodology is evaluated with a second catalytic application, that is, light paraffin isomerization. Finally, we discuss why SVMs is a strategic method, as compared to other machine learning techniques, such as neural networks or induction trees, and why emphasis is put on the problem of overfitting.
authors.affiliation: Instituto de Tecnología Química (UPV-CSIC), av. Naranjos s/n, 46022 Valencia, Spain.
authors.forename: L A
authors.initials: LA
authors.lastname: Baumes
authors.forename: J M
authors.initials: JM
authors.lastname: Serra
authors.forename: P
authors.initials: P
authors.lastname: Serna
authors.forename: A
authors.initials: A
authors.lastname: Corma
mesh_terms: D000475:Alkenes / Q000737:chemistry*; D002384:Catalysis; D003628:Database Management Systems; D016208:Databases, Factual; D005544:Forecasting; D007536:Isomerism; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010084:Oxidation-Reduction; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D000475:Alkenes