Universal Approximation Property of RKHS and Random Features (3)
We have seen the universal approximation property of RKHS generated by radial kernels and of one-hidden-layer neural networks with sigmoidal activation funct...
Recent Posts
Universal Approximation Property of RKHS and Random Features (2)
Universal Approximation Property of RKHS In this note, we discuss the universal approximation property of RKHS and compare with the property of neural networ...
Universal Approximation Property of RKHS and Random Features (1)
On a subset of , a binary symmetric function is called positive definite if the matrix is positive semi-definite for any list of elements . It is clear th...
Log Loss and Multi-Class Hinge Loss (1)
Hinge loss functions are mainly used in support vector machines for classification problem, while cross-entropy loss functions are ubiquitous in neural netwo...
Capacity of Neural Networks (5): Sauer’s Lemma
In this note we prove the Sauer’s lemma which plays the key role in establishing the connection between VC-dimension and Rademacher complexity. We use the pr...