Multiple kernel learning

Multiple Kernel Learning (MKL) is a machine learning approach that involves the use of multiple kernels to improve the performance of learning algorithms, particularly in situations where the data can be represented by different features or has varying characteristics. The central idea behind MKL is to combine different kernels, which are functions that compute a similarity or distance measure between data points in a possibly high-dimensional feature space.