Kernel learning is important in machine learning, but traditional kernels like Gaussian and polynomial are not flexible enough. The FKNDT method combines data-dependent and task-dependent kernels to create a more effective kernel for Support Vector Machines. By using a joint optimization approach, the researchers found that FKNDT outperformed traditional kernels and other state-of-the-art algorithms in terms of classification accuracy on fifteen benchmark datasets.