What are random features?
Randomized features provide a computationally efficient way to approximate kernel machines in machine learning tasks. However, such methods require a user-defined kernel as input. We extend the randomized-feature approach to the task of learning a kernel (via its associated random features).
What are random Fourier features?
Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds which are at odds with the empirical results.
What is random feature model?
Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to the real line.
What is kernel ridge regression?
Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear function in the original space.
How do kernels work?
The kernel performs its tasks, such as running processes, managing hardware devices such as the hard disk, and handling interrupts, in this protected kernel space. In contrast, application programs such as browsers, word processors, or audio or video players use a separate area of memory, user space.
What is isotropic kernel?
An isotropic Kernel is a kernel which depends only on the distance of the kernel arguments, K(x,y) = f(||x-y||) ||. || is any suitable norm, usually the L2-norm. Intuitively this means that the direction of the deviation is of no importance.
What is sigmoid kernel?
Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of the neural network, which is used as an activation function for artificial neurons.