In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitti...
Junzhou Huang, Shaoting Zhang, Dimitris N. Metaxas
We present a data structure for efficient axis-aligned orthogonal range search on a set of n lines in a bounded plane. The algorithm requires O(log n + k) time in the worst case t...
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
We explore the advantages of DNA-like genomes for evolutionary computation in silico. Coupled with simulations of chemical reactions, these genomes offer greater efficiency, reliab...
We present a new algorithm, called incremental least squares policy iteration (ILSPI), for finding the infinite-horizon stationary policy for partially observable Markov decision ...