Multiple Kernel

Multiple kernel learning (MKL) aims to improve machine learning models by combining information from multiple kernels, each capturing different aspects of the data. Current research focuses on developing efficient algorithms for MKL, particularly in high-dimensional or large-scale settings, often employing techniques like graph embeddings, recursive methods, and random feature approximations within various architectures including support vector machines, Gaussian processes, and neural networks. These advancements enhance the performance of MKL in diverse applications such as image classification, clustering, and time series forecasting, leading to improved accuracy and scalability. The ability to effectively integrate diverse data representations through MKL holds significant promise for advancing various fields.