An Epsilon Hierarchical Fuzzy Twin Support Vector Regression
  The research presents epsilon hierarchical fuzzy twin support vector
regression based on epsilon fuzzy twin support vector regression and epsilon
twin support vector regression. Epsilon FTSVR is achieved by incorporating
trapezoidal fuzzy numbers to epsilon TSVR which takes care of uncertainty
existing in forecasting problems. Epsilon FTSVR determines a pair of epsilon
insensitive proximal functions by solving two related quadratic programming
problems. The structural risk minimization principle is implemented by
introducing regularization term in primal problems of epsilon FTSVR. This
yields dual stable positive definite problems which improves regression
performance. Epsilon FTSVR is then reformulated as epsilon HFTSVR consisting of
a set of hierarchical layers each containing epsilon FTSVR. Experimental
results on both synthetic and real datasets reveal that epsilon HFTSVR has
remarkable generalization performance with minimum training time.
