Constructive higher-order network that is polynomial time
Neural Networks
Approximation and Estimation Bounds for Artificial Neural Networks
Machine Learning - Special issue on computational learning theory
A feedforward neural network with function shape autotuning
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Handling of incomplete data sets using ICA and SOM in data mining
Neural Computing and Applications
Time series prediction with single multiplicative neuron model
Applied Soft Computing
Sequence Data Mining (Advances in Database Systems)
Sequence Data Mining (Advances in Database Systems)
Financial time series prediction using polynomial pipelined neural networks
Expert Systems with Applications: An International Journal
Successes and New Directions in Data Mining
Successes and New Directions in Data Mining
ANSER: adaptive neuron artificial neural network system for estimating rainfall
International Journal of Computers and Applications
Long-term attraction in higher order neural networks
IEEE Transactions on Neural Networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Neuron-adaptive higher order neural-network models for automated financial data modeling
IEEE Transactions on Neural Networks
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An Artificial Neural Network (ANN) works by creating connections between different processing elements (artificial neurons). ANNs have been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks such as determining breast cancer recurrences and predicting incomes base on census data. An adaptive hyperbolic tangent function is used as the neuron activation function for the new adaptive HONN model. The paper compares the new HONN model against a Multi-Layer Perceptron (MLP) with the sigmoid activation function, an RBF Neural Network with the Gaussian activation function, and a Recurrent Neural Network (RNN) with the sigmoid activation function. Experimental results show that the new adaptive HONN model offers several advantages over conventional ANN models such as better generalisation capabilities as well as abilities in handling missing values in a dataset.