A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Covariance Matrix Estimation and Classification With Limited Training Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Processing of Remotely-Sensed Images: An Introduction
Computer Processing of Remotely-Sensed Images: An Introduction
Parallel consensual neural networks
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
International Journal of Remote Sensing - Remote Sensing: its Applications and Integration with GIS
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The performance of classification algorithms is heavily related to the quality of the training samples in supervised learning. Conventional statistical classifiers assume that data have a specific distribution. Such assumptions may not be valid for real world data. Additionally, enough training samples are required for every class to make a proper estimation of parameters to represent distribution functions. In general, there is a limited number of training samples in remote sensing. Therefore, classification algorithms should be robust with various types of training sample sets to achieve sufficient generalization performance. In this study, a new classification algorithm called border feature detection and adaptation (BFDA) is used to partition the feature space by taking into account some geometric considerations to support maximum margins between different class borders via some reference vectors called border features. The performance of the BFDA is related to the initialization of the border features during the border feature detection stage, and the input ordering of the training samples during the adaptation process. These dependencies cause relatively biased decisions. Therefore, consensual strategy with cross validation can be applied to improve the generalization performance. The resulting process is called consensual BFDA (C-BFDA).