A resource-allocating network for function interpolation
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Kernel PCA for novelty detection
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation
Engineering Applications of Artificial Intelligence
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN
IEEE Transactions on Neural Networks
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Expert Systems with Applications: An International Journal
A novel self-constructing Radial Basis Function Neural-Fuzzy System
Applied Soft Computing
The binary output units of neural network
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Expert Systems with Applications: An International Journal
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In this paper, we propose a sequential learning algorithm for a neural network classifier based on human meta-cognitive learning principles. The network, referred to as Meta-cognitive Neural Network (McNN). McNN has two components, namely the cognitive component and the meta-cognitive component. A radial basis function network is the fundamental building block of the cognitive component. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. When a sample is presented at the cognitive component of McNN, the meta-cognitive component chooses the best learning strategy for the sample using estimated class label, maximum hinge error, confidence of classifier and class-wise significance. Also sample overlapping conditions are considered in growth strategy for proper initialization of new hidden neurons. The performance of McNN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository and two practical problems, viz., the acoustic emission for signal classification and a mammogram data set for cancer classification. The statistical comparison clearly indicates the superior performance of McNN over reported results in the literature.