Name
Abstract | ||
|---|---|---|
Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward higher order neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72383-7_147 | ISNN (1) |
Keywords | Field | DocType |
new subsets,new adaptive neural network,function approximation,traditional neuron-fixed feed-forward network,data mining,reduced network size,biological neural network,new activation function,feed-forward higher order neural,neuron-adaptive activation function,financial data mining,artificial neural network,feed forward,process design,higher order,neural network,financial analysis,activation function | Data mining,Nervous system network models,Computer science,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
4491 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuxiang Xu | 1 | 67 | 12.73 |
| Ming Zhang | 2 | 15 | 2.80 |