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Everything about Hyper basis function network
In machine learning, a Hyper basis function network, or HyperBF network, is a generalization of radial basis function (RBF) networks concept, where the Mahalanobis-like distance is used instead of the Euclidean distance measure. Hyper basis function networks were first introduced by Poggio and Girosi in the 1990 paper “Networks for Approximation and Learning”. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Hyper basis function networkA 5-minute orientation
- Skim a recent paper: Modeling and analysis of variable fractional order hyper-chaotic LĂĽ system with radial basis function neural networksInfluential work since 2022
- Pick up a classic book: Introduction to making very big booksThe most-reprinted book on the subject
- Read the landmark paper: A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximationCited 51 times
- Go sideways: Cramér–Rao boundA closely related topic
- Go sideways: Toroidal coordinatesA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation
Topology Optimization Using Hyper Radial Basis Function Network
A survey on hyper basis function neural networks
An online learning algorithm with dimension selection using minimal hyper basis function networks
Design of compliant mechanism using hyper radial basis function network and reanalysis formulation
Adaptive Learning of Evolving Hyper Basis Function Neural Network
🆕 Influential since 2022
đź“š Books worth reading
Introduction to making very big booksUnited States. Congress. Senate. Committee on Pensions · 2011
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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