Selecting Inputs for Modeling Using Normalized Higher Order Statistics and Independent Component Analysis  

Oleh:

Back, A. D. ; Trappenberg, T. P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 3 (2001), page 612-617.
Topik: independent study; inputs; normalized higher order; statistics; independent component analysis

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

The problem of input variable selection is well known in the task of modeling real - world data. In this paper, we propose a novel model - free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches when the inputs are dependent.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali