Abstract
A novel boosting based perceptron learning algorithm is presented that uses AdaBoost along with a new representation of decision stumps using homogenous coordinates. The new representation of decision stumps makes perceptron an instance of boosting based ensemble. As Boostron minimizes an exponential cost function instead of the mean squared error minimized by the perceptron learning algorithm, it gives improved performance for classification problems. The proposed method is compared to the perceptron learning algorithm using several classification problems of varying complexity.
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Baig, M.M., Awais, M.M., El-Alfy, ES.M. (2014). BOOSTRON: Boosting Based Perceptron Learning. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_25
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DOI: https://doi.org/10.1007/978-3-319-12637-1_25
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