Deep Kernel Learning
Tags: #machine learning #Deep Kernel LearningEquation
$$k(x_{i},x_{j}|\phi)=k(h(x_i,w_k),h(x_j,w_k)|w_k,\phi)$$Latex Code
k(x_{i},x_{j}|\phi)=k(h(x_i,w_k),h(x_j,w_k)|w_k,\phi)
Have Fun
Let's Vote for the Most Difficult Equation!
Introduction
Equation
Latex Code
k(x_{i},x_{j}|\phi)=k(h(x_i,w_k),h(x_j,w_k)|w_k,\phi)
Explanation
The original data instance x_{i} is first mapped to latent space by a non-linear transformation h(x_{i}, w_{k}), usually a deep neural network with parameter w_{k}, and then passed to a kernel function k(x_{i},x_{j}|\phi). See below link Deep Kernel Learning for more details.
Reply