title: Comments on "Pruning error minimization in least squares support vector machines".
abstract: In this letter, we comment on "Pruning Error Minimization in Least Squares Support Vector Machines" by B. J. de Kruif and T. J. A. de Vries. The original paper proposes a way of pruning training examples for least squares support vector machines (LS SVM) using no regularization (-gamma = infinity). This causes a problem as the derivation involves inverting a matrix that is often singular. We discuss a modification of this algorithm that prunes with regularization (gamma finite and nonzero) and is also computationally more efficient.
authors: Anthony
authors: A
authors: Kuh
authors: Philippe
authors: P
authors: De Wilde
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D016247:Information Storage and Retrieval / Q000379:methods*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*