Multiple Kernel Learning is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep learning models and are inferior to these models in terms of recognition accuracy. Deep learning models can learn complex functions by applying nonlinear transformations to data through several layers. In this paper, we show that a typical MKL algorithm can be interpreted as a one-layer neural network with linear activation functions. By this interpretation, we propose a Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the conventional multiple kernel learning framework to a multi-layer neural network with nonlinear activation functions. Our experiments on several benchmarks show that the proposed method improves the complexity of MKL algorithms and leads to higher recognition accuracy.

翻译：多核学习是基于核方法中学习核函数的传统方式。MKL算法提升了核方法的性能。然而，与深度学习模型相比，这些方法复杂度较低，且在识别精度上逊于深度学习模型。深度学习模型通过多层对数据施加非线性变换，能够学习复杂函数。本文表明，典型的MKL算法可被解释为具有线性激活函数的单层神经网络。基于此解释，我们提出神经多核学习的泛化（NGMKL），将传统多核学习框架扩展为具有非线性激活函数的多层神经网络。在多个基准数据集上的实验表明，所提方法提升了MKL算法的复杂度，并取得了更高的识别精度。