Principal Feature Classification  

Oleh:

Li, Qi ; Tufts, D. W.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 1 (1997), page 155-160.
Topik: principal component analysis; principal; feature; classification

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

The concept, structures, and algorithms of principal feature classification (PFC) are presented in this paper. PFC is intended to solve complex classification problems with large data sets. A PFC network is designed by sequentially finding principal features and removing training data which has already been correctly classified. PFC combines advantages of statistical pattern recognition, decision trees, and artificial neural networks (ANN s) and provides fast learning with good performance and a simple network structure. For the real - world applications of this paper, PFC provides better performance than conventional statistical pattern recognition, avoids the long training times of backpropagation and other gradient - descent algorithms for ANN s, and provides a low - complexity structure for realization.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali