Approximation by neural networks and learning theory
نویسندگان
چکیده
منابع مشابه
Approximation Theory and Neural Networks
In many practical situations, one needs to construct a model for an input/output process. For example, one is interested in the price of a stock five years from now. The rating industry description for the stock typically lists such indicators as the increase in the price over the last year, the last 5 years, 10 years, life of the stock, P/E ratio, and alpha and beta risk factors. The buyer is ...
متن کاملimproving multilayer back propagation neural networks by using variable learning rate and automata theory and determining optimum learning rate
multilayer bach propagation neural networks have been considered by researchers. despite their outstanding success in managing contact between input and output, they have had several drawbacks. for example the time needed for the training of these neural networks is long, and some times not to be teachable. the reason for this long time of teaching is due to the selection unsuitable network par...
متن کاملApproximation by Ridge Functions and Neural Networks
We investigate the efficiency of approximation by linear combinations of ridge functions in the metric of L2(B ) with Bd the unit ball in Rd. If Xn is an n-dimensional linear space of univariate functions in L2(I), I = [−1, 1], and Ω is a subset of the unit sphere Sd−1 in Rd of cardinality m, then the space Yn := span{r(x · ξ) : r ∈ Xn, ω ∈ Ω} is a linear space of ridge functions of dimension ≤...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Complexity
سال: 2006
ISSN: 0885-064X
DOI: 10.1016/j.jco.2005.09.001