An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Orthogonality of decision boundaries in complex-valued neural networks
Neural Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation
Engineering Applications of Artificial Intelligence
Data gravitation based classification
Information Sciences: an International Journal
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Automated assessment of breast tissue density in digital mammograms
Computer Vision and Image Understanding
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Letters: Fully complex extreme learning machine
Neurocomputing
The computational power of complex-valued neuron
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Channel equalization using neural networks: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Engineering Applications of Artificial Intelligence
Calibrated lazy associative classification
Information Sciences: an International Journal
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN
IEEE Transactions on Neural Networks
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Complex-Valued neuro-fuzzy inference system based classifier
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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In this paper, we present a fast learning fully complex-valued extreme learning machine classifier, referred to as 'Circular Complex-valued Extreme Learning Machine (CC-ELM)' for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational/rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like ('sech') activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. This paper also presents an analytical proof to show that the decision boundaries of a single complex-valued neuron at the hidden and output layers of CC-ELM consist of two hyper-surfaces that intersect orthogonally. These orthogonal boundaries and the input circular transformation help CC-ELM to perform real-valued classification tasks efficiently. Performance of CC-ELM is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository. Finally, the performance of CC-ELM is compared with existing methods on two practical problems, viz., the acoustic emission signal classification problem and a mammogram classification problem. These study results show that CC-ELM performs better than other existing (both) real-valued and complex-valued classifiers, especially when the data sets are highly unbalanced.