✦ LIBER ✦

An adaptive conjugate gradient learning algorithm for efficient training of neural networks

✍ Scribed by H. Adeli; S.L. Hung

Book ID: 107884655
Publisher: Elsevier Science
Year: 1994
Tongue: English
Weight: 1003 KB
Volume: 62
Category: Article
ISSN: 0096-3003
DOI: 10.1016/0096-3003(94)90134-1

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

An efficient improved adaptive genetic a

An efficient improved adaptive genetic algorithm for training layered feedforward neural networks

✍ Wang Xin-miao; Yan Pu-liu; Huang Tian-xi 📂 Article 📅 1999 🏛 Wuhan University 🌐 English ⚖ 51 KB

An efficient global algorithm for superv

An efficient global algorithm for supervised training of neural networks

✍ K.K. Shukla; Raghunath 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 285 KB

A parallel algorithm for gradient traini

A parallel algorithm for gradient training of feedforward neural networks

✍ Zdeněk Hanzálek 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 742 KB

This paper presents a message-passing architecture simulating multilayer neural networks, adjusting its weights for each pair, consisting of an input vector and a desired output vector. First, the multilayer neural network is defined, and the difficulties arising from parallel implementation are cla

An Adaptive Conjugate Gradient Neural Ne

An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

✍ H. Adeli; A. Samant 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 367 KB

Adaptive stepsize algorithms for on-line

Adaptive stepsize algorithms for on-line training of neural networks

✍ G.D. Magoulas; V.P. Plagianakos; M.N. Vrahatis 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 312 KB

In this paper a method for adapting the stepsize in on-line network training is presented. The proposed technique derives from the stochastic gradient descent proposed by Almeida et al. [On-line Learning in Neural Networks, 111-134, Cambridge University Press, 1998]. The new aspect of our approach c

Efficient Partition of Learning Data Set

Efficient Partition of Learning Data Sets for Neural Network Training

✍ Igor V. Tetko; Alessandro E.P. Villa 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 641 KB

This study investigates the emerging possibilities of combining unsupervised and supervised learning in neural network ensembles. Such strategy is used to get an efficient partition of a noisy input data set in order to focus the training of neural networks on the most complex and informative domain