1997 UnsupervisedQuerybasedLearningo

• (Chang & Hsiao, 1997) ⇒ Ray-I Chang, and Pei-Yung Hsiao. (1997). “Unsupervised Query-based Learning of Neural Networks Using Selective-attention and Self-regulation.” In: IEEE Transactions on Neural Networks Journal, 8(2). doi:10.1109/72.557657

Subject Headings: Query-Based Learning System, Unsupervised Query-Based Learning System.

Cited By

• Google Scholar: 20 Citations
• ACM DL: 5 Citations
• Semantic Scholar: 9 Citations

Quotes

Author Keywords

• Force-directed method; query-based method; selective attention; self-organizing maps; self regulation; unsupervised learning.

Abstract

Query-based learning (QBL) has been introduced for training a supervised network model with additional queried samples. Experiments demonstrated that the classification accuracy is further increased. Although QBL has been successfully applied to supervised neural networks, it is not suitable for unsupervised learning models without external supervisors. In this paper, an unsupervised QBL (UQBL) algorithm using selective-attention and self-regulation is proposed. Applying the selective-attention, we can ask the network to respond to its goal-directed behavior with self-focus. Since there is no supervisor to verify the self-focus, a compromise is then made to environment-focus with self-regulation. In this paper, we introduce UQBL1 and UQBL2 as two versions of UQBL; both of them can provide fast convergence. Our experiments indicate that the proposed methods are more insensitive to network initialization. They have better generalization performance and can be a significant reduction in their training size

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1997 UnsupervisedQuerybasedLearningo	Ray-I Chang Pei-Yung Hsiao			Unsupervised Query-based Learning of Neural Networks Using Selective-attention and Self-regulation				10.1109/72.557657		1997