This paper investigates the ability of several models of Support Vector Machines (SVMs) with alternate kernel functions to predict the probability of occurrence of Essential Hypertension (HT) in a mixed patient population. To do this a SVM was trained with 13 inputs (symptoms) from the medical dataset. Different kernel functions, such as Linear, Quadratic, Polyorder (order three), Multi Layer P...

Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing the intersecting research. In this paper, we introduce a new research problem, termed Online Multiple Kernel Learning (OMKL), that aims to learn a kernel based prediction function from a pool of predefined kernels in ...

Vertex Cover is one of the most well studied problems in the realm of parameterized algorithms and admits a kernel with O(`2) edges and 2` vertices. Here, ` denotes the size of a vertex cover we are seeking for. A natural question is whether Vertex Cover admits a polynomial kernel (or a parameterized algorithm) with respect to a parameter k, that is, provably smaller than the size of the vertex...

FAUST is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model (Sec. 1) is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure (Sec. 2) runs in MATLAB and employs parallel computations and fast m...

0 C(t, s)x(s)ds with sharply contrasting kernels typified by C∗(t, s) = ln(e + (t − s)) and D∗(t, s) = [1 + (t − s)]. The kernel assigns a weight to x(s) and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature. Our first project is to show that for a ∈ L[0,∞), then solutions are largely indistinguishable regardless of which kernel is used. T...

This document contains detailed proofs of theorems stated in the main article entitled Random Feature Maps for Dot Product Kernels. 1 Proof of Theorem 1 We first recollect Schoenberg’s result in its original form Theorem 1 (Schoenberg (1942), Theorem 2). A function f : [−1, 1]→ R constitutes a positive definite kernel K : S∞ × S∞ → R, K : (x,y) 7→ f(〈x,y〉) iff f is an analytic function admittin...

s from Journal of Machine Learning (JMLR) reproduce kernel hilbert space support vector machin svm

Then, two points p, q of an orthogonal polygon P are s-visible from one another if there exists a stair ase path from p to q that lies in P (Figure 1(a) shows two su h points p and q). The set of points that are s-visible from a point p form the s-visibility polygon of p. The s-kernel of P is the (possibly empty) set of points of P whose s-visibility polygon is equal to P , i.e., the set of poi...