Abstract
Soft decision trees, aka hierarchical mixture of experts, are composed of soft multivariate decision nodes and output-predicting leaves. Previously, they have been shown to work successfully in supervised classification and regression tasks, as well as in training unsupervised autoencoders. This work has two contributions: First, we show that dropout and dropconnect on input units, previously proposed for deep multi-layer neural networks, can also be used with soft decision trees for regularization. Second, we propose a convolutional extension of the soft decision tree with local feature detectors in successive layers that are trained together with the other parameters of the soft decision tree. Our experiments on four image data sets, MNIST, Fashion-MNIST, CIFAR-10 and Imagenet32, indicate improvements due to both contributions.
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Acknowledgements
The numerical calculations are performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).
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Ahmetoğlu, A., İrsoy, O., Alpaydın, E. (2018). Convolutional Soft Decision Trees. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_14
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DOI: https://doi.org/10.1007/978-3-030-01418-6_14
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