Brief paper: Identification of dynamic systems using Piecewise-Affine basis function models
Automatica (Journal of IFAC)
Letters: Convex incremental extreme learning machine
Neurocomputing
Brief paper: Adaptive hinging hyperplanes and its applications in dynamic system identification
Automatica (Journal of IFAC)
The hill detouring method for minimizing hinging hyperplanes functions
Computers and Operations Research
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
Identification of piecewise affine systems via mixed-integer programming
Automatica (Journal of IFAC)
Hinging hyperplane models for multiple predicted variables
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Identification of Hammerstein-Wiener models
Automatica (Journal of IFAC)
Hinging hyperplane based regression tree identified by fuzzy clustering and its application
Applied Soft Computing
Identification of switched linear regression models using sum-of-norms regularization
Automatica (Journal of IFAC)
Multivariate convex regression with adaptive partitioning
The Journal of Machine Learning Research
| Hi-index | 754.85 |
A hinge function y=h(x) consists of two hyperplanes continuously joined together at a hinge. In regression (prediction), classification (pattern recognition), and noiseless function approximation, use of sums of hinge functions gives a powerful and efficient alternative to neural networks with computation times several orders of magnitude less than is obtained by fitting neural networks with a comparable number of parameters. A simple and effective method for finding good hinges is presented