Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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Oration

,FFTRL: A Sparse Online Kernel Classification Algorithm for Large Scale Data,ion in a linear manner. The regret bound analysis shows the feasibility of FFTRL in theory. Comprehensive experiments were carried out on public datasets to compare the performance of FFTRL with related online kernel algorithms. Promising results show that our proposed method enjoys both high accura

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,Gaze Behavior Patterns for Early Drowsiness Detection, from the gaze behavior features. We conducted experiments on the largest publicly available multi-stage drowsiness video dataset RLDD. Preliminary analysis of the dataset showed the distribution of the features of our selected gaze behavior patterns over different drowsiness stages had relatively s

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Artificial Neural Networks and Machine Learning – ICANN 202332nd International C

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Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay

Sinus-Rhythm

G. J. Wullems,J. A. M. Schrauwenthe proposed measure and recognition accuracy in a multi-task scenario constructed from a real dataset. Finally, we discuss the methods for evaluating the classification performance of machine learning and deep learning models considering data separability.

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Immunization

A. Tsafriri,S. Bar-Ami,H. R. Lindner values of kernel size, number of filters and of neurons by using the German Traffic Sign Recognition Benchmark (GTSRB) for training. As a result, we propose BNNs architectures which achieve an accuracy of more than . for GTSRB (the maximum is .) and an average greater than . (the maximum is .) cons

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A. Lopata,D. Kohlman,I. Johnstonprove feature representation. We performed a lot of experiments on the PASCAL VOC 2012 Action dataset and the Stanford 40 Actions dataset. The results demonstrate that our method performs effectively, with the state-of-the-arts outcomes being obtained on both datasets.