Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression
IEEE Transactions on Neural Networks
Asymmetric least squares support vector machine classifiers
Computational Statistics & Data Analysis
Mean field variational Bayesian inference for support vector machine classification
Computational Statistics & Data Analysis
Pattern Recognition Letters
| Hi-index | 0.03 |
A mixed effects least squares support vector machine (LS-SVM) classifier is introduced to extend the standard LS-SVM classifier for handling longitudinal data. The mixed effects LS-SVM model contains a random intercept and allows to classify highly unbalanced data, in the sense that there is an unequal number of observations for each case at non-fixed time points. The methodology consists of a regression modeling and a classification step based on the obtained regression estimates. Regression and classification of new cases are performed in a straightforward manner by solving a linear system. It is demonstrated that the methodology can be generalized to deal with multi-class problems and can be extended to incorporate multiple random effects. The technique is illustrated on simulated data sets and real-life problems concerning human growth.