Tag Archives: Laplacian

前面幾篇文章提到 manifold learning, 只討論用於降維，沒有討論如何用於 learning. 什麼是 learning? No free lunch theorem told us need to (1) underline structure; (2) constraints to learn something from existing data. underline structure => manifold hypothesis or kernel method, 如下式的 (function search space). constraint => Occam razor principle, simpler is better than complex, smooth (underfit or just make) is better …

Continue reading “Math AI – Manifold Learning 數學基礎”