Nu-SVR Learning with Predetermined Basis Functions Included

Computing and stopping the solution paths for $\nu$-SVR

Gestalt Pattern Recognition with Arrays of Predetermined Neural Functions

A p r e d e t e r m i n e d n e u r a l l i k e f u n c t i o n wh ich makes a t e r n a r y l o g i c a l compar ison t o i t s n e i g h b o r s i n a n a r r a y o f s i m i l a r f u n c t i o n s e x h i b i t s t he a b i l i t y t o c a t e g o r i z e t w o d i m e n s i o n a l geomet r i c p a t t e r n s b y t he d i s t i n c t i v e n e s s o f t h e i r shape d i r e c t l y . T h...

MQ-Radial Basis Functions Center Nodes Selection with PROMETHEE Technique

In this paper‎, ‎we decide to select the best center nodes‎ ‎of radial basis functions by applying the Multiple Criteria Decision‎ ‎Making (MCDM) techniques‎. ‎Two methods based on radial basis‎ ‎functions to approximate the solution of partial differential‎ ‎equation by using collocation method are applied‎. ‎The first is based‎ ‎on the Kansa's approach‎, ‎and the second is based on the Hermit...

Learning Basis Functions in Hybrid Domains

Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea of the approach is to approximate the optimal value function by a set of basis functions and optimize their weights by linear programming. The quality of this approximation naturally depends on its basis functions. How...

Multilayer Perceptrons with Radial Basis Functions as Value Functions in Reinforcement Learning

Using multilayer perceptrons (MLPs) to approximate the state-action value function in reinforcement learning (RL) algorithms could become a nightmare due to the constant possibility of unlearning past experiences. Moreover, since the target values in the training examples are bootstraps values, this is, estimates of other estimates, the chances to get stuck in a local minimum are increased. The...