Kernel Support Vector Regression with imprecise output

We consider a regression problem where uncertainty affects to the dependent variable of the elements of the database. A model based on the standard epsilon-Support Vector Regression approach is given, where two hyperplanes need to be constructed to predict the interval-valued dependent variable. By using the Hausdorff distance to measure the error between predicted and … Read more

A Q-Learning Algorithm with Continuous State Space

We study in this paper a Markov Decision Problem (MDP) with continuous state space and discrete decision variables. We propose an extension of the Q-learning algorithm introduced to solve this problem by Watkins in 1989 for completely discrete MDPs. Our algorithm relies on stochastic approximation and functional estimation, and uses kernels to locally update the … Read more

Temporal difference learning with kernels for pricing american-style options

We propose in this paper to study the problem of estimating the cost-to-go function for an infinite-horizon discounted Markov chain with possibly continuous state space. For implementation purposes, the state space is typically discretized. As soon as the dimension of the state space becomes large, the computation is no more practicable, a phenomenon referred to … Read more

Kernels in planar digraphs

A set $S$ of vertices in a digraph $D=(V,A)$ is a kernel if $S$ is independent and every vertex in $V-S$ has an out-neighbour in $S$. We show that there exists an $O(3^{\delta \sqrt{k}} n)$~% \footnote{Throughout this paper the constants $\delta$ and $c$ are the same as the comparative constants mentioned in~\cite{kn:alber}.} algorithm to check … Read more