Facial emotion recognition by adaptive processing of tree structures
Proceedings of the 2006 ACM symposium on Applied computing
Probabilistic based recursive model for adaptive processing of data structures
Expert Systems with Applications: An International Journal
A local experts organization model with application to face emotion recognition
Expert Systems with Applications: An International Journal
Segmented-memory recurrent neural networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Tree structures with attentive objects for image classification using a neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Probabilistic based recursive model for face recognition
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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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.