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|Title: ||An improved algorithm for learning long-term dependency problems in adaptive processing of data structures|
|Authors: ||Cho, Siu-Yeung|
Chi, Zheru George
Tsoi, Ah Chung
|Subjects: ||Adaptive processing of data structures|
Back-propagation through structure (BPTS)
|Issue Date: ||Jul-2003 |
|Citation: ||IEEE Transactions on neural networks, July 2003, v. 14, no. 4, p. 781-793.|
|Abstract: ||For the past decade, many researchers have explored the use of neural-network representations for the adaptive processing
of data structures. One of the most popular learning formulations of data structure processing is backpropagation through
structure (BPTS). The BPTS algorithm has been successful applied to a number of learning tasks that involve structural patterns such as logo and natural scene classification. The main limitations of
the BPTS algorithm are attributed to slow convergence speed and the long-term dependency problem for the adaptive processing of data structures. In this paper, an improved algorithm is proposed
to solve these problems. The idea of this algorithm is to optimize the free learning parameters of the neural network in the node representation by using least-squares-based optimization methods in a layer-by-layer fashion. Not only can fast convergence speed be achieved, but the long-term dependency problem can also be overcome since the vanishing of gradient information is avoided when our approach is applied to very deep tree structures.|
|Rights: ||© 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
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|Type: ||Journal/Magazine Article|
|Appears in Collections:||EIE Journal/Magazine Articles|
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