Ergebnisse für: learning vector-valued functions
Hier findest Du Bücher, die sich mit learning vector-valued functions beschäftigen.
Kernel-based methods and their underlying structure of reproducing kernel Hilbert spaces (RKHS) are widely used in many areas of applied mathematics, such as spatial statistics, machine learning and approximation theory. In this thesis, we provide an overview over RKHS of vector-valued functions and...
In this thesis, we show how kernel regularization approaches can be used for parameter estimation in different multidimensional systems. We study this problem in the context of reproducing kernel Hilbert spaces of vector-valued functions. This also allows interpretation in terms of Gaussian processe...
Neutrosophy (1995) is a new branch of philosophy that studies triads of the form (, , ), where is an entity {i.e. element, concept, idea, theory, logical proposition, etc.}, is the opposite of , while is the neutral (or indeterminate) between them, i.e., neither nor .Based on neutrosophy, the ne...
Neutrosophy (1995) is a new branch of philosophy that studies triads of the form (, , ), where is an entity {i.e. element, concept, idea, theory, logical proposition, etc.}, is the opposite of , while is the neutral (or indeterminate) between them, i.e., neither nor .Based on neutrosophy, the ne...