LiMin Fu
University of Florida
5 Papers
72 Citations
LiMin Fu is an academic researcher from University of Florida. The author has contributed to research in topics: Artificial neural network & Activation function. The author has an hindex of 4, co-authored 5 publications.
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Papers
Incremental backpropagation learning networks
TL;DR: A new incremental learning method for pattern recognition is presented, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process.
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A neural-network model for learning domain rules based on its activation function characteristics
TL;DR: The CFNet is described, which bases its activation function on the certainty factor (CF) model of expert systems, and a new analysis on the computational complexity of rule learning in general is provided.
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Learning in certainty-factor-based multilayer neural networks for classification
TL;DR: It is shown theoretically that the neural network using the CF-based activation function requires relatively small sample sizes for correct generalization.
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Learning capacity and sample complexity on expert networks
TL;DR: This paper addresses a yet-to-be-answered question: Why can expert networks generalize more effectively from a finite number of training instances than multilayered perceptrons?
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Discrete probability estimation for classification using certainty-factor-based neural networks
TL;DR: A new analysis presented here shows that the basis functions learned by the CFNet can bear precise semantics for dependencies.
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