论文标题
将深度学习与良好的老式机器学习相结合
Combining Deep Learning with Good Old-Fashioned Machine Learning
论文作者
论文摘要
我们提出了一个全面的，基于堆叠的框架，用于将深度学习与良好的老式机器学习（称为深金）相结合。我们的框架涉及从51个经过预定的深网作为第一级模型和10种机器学习算法作为二级模型的合奏选择。通过当今最先进的软件工具和硬件平台启用，深金在四个图像分类数据集进行测试时会提供一致的改进：时尚MNIST，CIFAR10，CIFAR100和TINY IMAGENET。在120个实验中，除10种深金黄金外，还提高了原始网络的性能。
We present a comprehensive, stacking-based framework for combining deep learning with good old-fashioned machine learning, called Deep GOld. Our framework involves ensemble selection from 51 retrained pretrained deep networks as first-level models, and 10 machine-learning algorithms as second-level models. Enabled by today's state-of-the-art software tools and hardware platforms, Deep GOld delivers consistent improvement when tested on four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original networks' performance.
