Globally Convergent Algorithms With Local Learning Rates  

Oleh:

Magoulas, G. D. ; Plagianakos, V. P. ; Vrahatis, M. N.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 3 (2002), page 774-779.
Topik: algorithms; globally; convergent algorithms; local learning rates

Ketersediaan

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

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Isi artikel

A novel generalized theoretical result is presented that underpins the development of globally convergent first -order batch training algorithms which employ local learning rates. This result allows us to equip algorithms of this class with a strategy for adapting the overall direction of search to a descent one. In this way, a decrease of the batch - error measure at each training iteration is ensured, and convergence of the sequence of weight iterates to a local minimizer of the batch error function is obtained from remote initial weights. The effectiveness of the theoretical result is illustrated in three application examples by comparing two well - known training algorithms with local learning rates to their globally convergent modifications.

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