Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to artificial neural systems
Introduction to artificial neural systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Improvement on Higher-Order Neural Networks for Invariant Object Recognition
Neural Processing Letters
On a higher-order neural network for distortion invariant pattern recognition
Pattern Recognition Letters
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
Multi-class pattern classification using neural networks
Pattern Recognition
Handling of incomplete data sets using ICA and SOM in data mining
Neural Computing and Applications
Multiorder neurons for evolutionary higher-order clustering and growth
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Artificial Higher Order Neural Networks for Economics and Business
Artificial Higher Order Neural Networks for Economics and Business
Dynamic Ridge Polynomial Neural Networks in Exchange Rates Time Series Forecasting
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Application of New Adaptive Higher Order Neural Networks in Data Mining
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Fuzzy classification systems based on fuzzy information gain measures
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A neural network-based multi-agent classifier system
Neurocomputing
Hybrid high order neural networks
Applied Soft Computing
Sequence Data Mining
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive high-order neural tree for pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Neuron-adaptive higher order neural-network models for automated financial data modeling
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper, we propose a novel Hybrid Higher Order Neural Classifier (HHONC) which contains different high-order units. In contrast with conventional fully-connected higher order neural networks (HONN), our proposed method uses fewer learning parameters and allocates the best fitted model in dealing with different datasets by modifying the orders of different high-order units and updating the learning parameters. Structure, model selection and updating the learning parameters of HHONC is introduced and is applied in classification of the Iris data set, the breast cancer data set, the Wine recognition data set, the Glass identification data set, the Balance scale data set, and the Pima diabetes data set. Acquired results are compared with the methods presented in Chen and Shie (2009). It is observed that the fewer features the dataset contains, the more accurate the HHONC performs, however the accuracy of datasets with more features are acceptable. Experimental results show about 3.5% and 0.6% improvements compared to the best accuracy obtained in previously methods for classifying the Pima diabetes and Iris datasets, respectively. In addition, by using a same method for reducing the feature number, it's shown the proposed method perform more accurate than methods presented in Shie and Chen (2008). In this case, improvements compared to the best acquired accuracy of mentioned methods are about 1.7%, 1.3% and 0.2% in classification of Pima, Iris and Breast cancer datasets, respectively.