Asymmetric Kernel Regression  

Oleh:

Tieu, A. K. ; Mackenzie, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 2 (Mar. 2004), page 276-282.
Topik: kernel estimator; asymmetric; kernel regression

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Kernel regression is one model that has been applied to explain or design radial - basis neural networks. Practical application of the kernel regression method has shown that bias errors caused by the boundaries of the data can seriously effect the accuracy of this type of regression. This paper investigates the correction of boundary error by substituting an asymmetric kernel function for the symmetric kernel function at data points close to the boundary. The asymmetric kernel function allows a much closer approach to the boundary to be achieved without adversely effecting the noise - filtering properties of the kernel regression.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali