基于二阶锥规划的卷积神经网络收敛速度的自动选择
Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming
摘要：
包围盒技术在机器学习和工程问题中经常遇到，因为该方法结合了不同的模型并产生最优的预测解。集成概念可以适用于深度学习模型，以提供鲁棒性和可靠性。由于深度学习中模型的增长，使用集成修剪对于处理计算复杂性非常重要。因此，本研究提出了一种数学模型，该模型修剪了由不同深度和层组成的卷积神经网络（CNN）的集合，同时使用稀疏二阶圆锥优化模型最大化准确性和多样性。该模型在CIFAR-10、CIFAR-100和MNIST数据集上进行了测试，结果令人满意，同时显著降低了模型的复杂性。
英文摘要：
Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to deep learning models to provide robustness and reliability. Due to the growth of the models in deep learning, using ensemble pruning is highly important to deal with computational complexity. Hence, this study proposes a mathematical model which prunes the ensemble of Convolutional Neural Networks (CNN) consisting of different depths and layers that maximizes accuracy and diversity simultaneously with a sparse second order conic optimization model. The proposed model is tested on CIFAR-10, CIFAR-100 and MNIST data sets which gives promising results while reducing the complexity of models, significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
