Minimizing Risk Using Prediction Uncertainty in Neural Network Estimation Fusion and Its Application to Papermaking  

Oleh:

Renshaw, D. ; Edwards, P. J. ; Peacock, A. M. ; Hannah, J. M. ; Murray, A. F.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 3 (2002), page 726-731.
Topik: FUSION; minimizing risk; prediction; neural network; estimation; fusion; papermaking

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

The paper presents Bayesian information fusion theory in the context of neural - network model combination. It shows how confidence measures can be combined with individual model estimates to minimize risk through the fusion process. The theory is illustrated through application to the real task of quality prediction in the papermaking industry. Prediction uncertainty estimates are calculated using approximate Bayesian learning. These are incorporated into model combination as confidence measures. Cost functions in the fusion center are used to control the influence of the confidence measures and improve the performance of the resultant committee.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali