The emergence of deep learning frameworks paves the way for achieving higher-level data abstractions and possess the potential in consolidating both supervised and unsupervised learning paradigms. Researchers have made many successful explorations in the field of deep learning, with applications in the fields of face recognition, text mining, language translation, image prediction, and action recognition. Kernel machines act as a bridge between the linearity and nonlinearity for many machine learning algorithms such as support vector machines, extreme learning machines, and core vector machines. These Kernel machines play a vital role in mapping the data in the input space to a Kernel-induced high-dimensional feature space to obtain a better distribution of the data. In this Kernel-induced high-dimensional feature space, the distribution of data points will be more amenable to the classification problem under consideration. The Kernel trick facilitates in transforming the machine learning algorithms that require only inner product computations between the data vectors into a Kernel-based approach by selecting an appropriate Kernel function. In Kernel-based approaches, the Kernel functions can thus be utilized for accomplishing the inner product computations between the transformed data vectors in an implicitly defined Kernel-induced feature space. Unlike neural networks, the Kernel machines guarantee structural risk minimization and global optimal solutions. Also, the Kernel machines exhibit capabilities such as theoretical tractability and excellent performance in practical applications. These attempts motivated the researchers towards utilizing the emerging trends of deep learning with Kernel methods for building deep Kernel machines. Researchers integrate Kernel methods and deep learning networks for maintaining their advantages and make up their limitations, then apply the deep Kernel learning approaches for improving the performance of the learning algorithm in different applications. Different ways of building deep Kernel machines by integrating the Kernel methods and deep learning architectures include utilizing Kernel machines as the final classifier of deep learning networks, Kernelization in deep neural networks for better feature enrichment, and building deep Kernel machines by utilizing deep or multiple Kernels in different tasks. This survey attempts to provide an overview of different approaches in building several deep Kernel learning architectures for enhancing the learning algorithm properties and their performance in practical applications.

深度学习框架的出现为实现更高级别的数据抽象铺平了道路，并有可能整合有监督和无监督学习范例。研究人员在深度学习领域进行了许多成功的探索，并将其应用于面部识别，文本挖掘，语言翻译，图像预测和动作识别等领域。内核机器充当许多机器学习算法（例如支持向量机，极限学习机器和核心向量机）的线性和非线性之间的桥梁。这些内核机器在将输入空间中的数据映射到内核诱发的高维特征空间以获得更好的数据分布方面起着至关重要的作用。在此内核诱发的高维特征空间中，数据点的分布将更适合所考虑的分类问题。内核技巧通过选择合适的内核函数，有助于将仅需要数据向量之间的内积计算的机器学习算法转换为基于内核的方法。因此，在基于内核的方法中，内核函数可用于在隐式定义的内核诱发的特征空间中完成转换后的数据向量之间的内积计算。与神经网络不同，内核机器保证结构风险最小化和全局最优解决方案。此外，内核机器在实际应用中还具有诸如理论上的可处理性和出色的性能等功能。这些尝试激发了研究人员利用利用内核方法进行深度学习的新兴趋势来构建深度内核机器的动机。研究人员将内核方法和深度学习网络集成在一起，以保持其优势并弥补其局限性，然后应用深度内核学习方法来提高学习算法在不同应用程序中的性能。通过集成内核方法和深度学习架构来构建深度内核机器的不同方法包括：利用内核机器作为深度学习网络的最终分类器；深度神经网络中的内核化，以更好地丰富功能；以及通过利用深度或多个内核来构建深度内核机器。在不同的任务中。