 Kernel methods for vector output

Kernel methods are a class of techniques primarily used in machine learning for tasks involving linear transformations of data into higher-dimensional spaces through the kernel trick. They are especially well-known for their applications in support vector machines (SVMs) and regression problems. While many discussions around kernel methods focus on scalar outputs (e.g., classification or regression tasks predicting a single outcome), kernel methods can also be extended to handle vector outputs. ### Kernel Methods for Vector Output 1.