Cascade-Correlation Learning for Classification  

Oleh:

Lehtokangas, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 3 (2000), page 795-798.
Topik: LEARNING; cascade; correlation; learning; classification

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

The main advantages of cascade - correlation learning are the abilities to learn quickly and to determine the network size. However, recent studies have shown that in many problems the generalization performance of a cascade -correlation trained network may not be quite optimal. Moreover, to reach a certain performance level, a larger network may be required than with other training methods. Recent advances in statistical learning theory emphasize the importance of a learning method to be able to learn optimal hyperplanes. This has led to advanced learning methods, which have demonstrated substantial performance improvements. Based on these recent advances in statistical learning theory, we introduce modifications to the standard cascade - correlation learning that take into account the optimal hyperplane constraints. Experimental results demonstrate that with modified cascade correlation, considerable performance gains are obtained compared to the standard cascade - correlation learning. This includes better generalization, smaller network size, and faster learning.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali