We address the phase retrieval problem with errors in sensing vectors. A number of recent methods for are based on least squares (LS) formulations which assume quadratic measurements. extend this approach to handle vectors by adopting total (TLS) framework that is used linear inverse problems operator errors. show how gradient descent and specific geometry can be obtain a simple efficient TLS s...

A method for the construction of open approximate models from vector time series Preface PhD research is a largely open proces in which a stimulating environment plays a crucial role. I am indebted to several people and institutions that shaped such an environment for me during the years since April 1990. First and for all, I would like to thank my supervisor, Christiaan Heij. He set me on the ...

In this paper, we present a new block replacement policy in which we proposed a new efficient algorithm for combining two important policies Least Recently Used (LRU) and Least Frequently Used (LFU). The implementation of the proposed policy is simple. It requires limited calculations to determine the victim block. We proposed our models to implement LRU and LFU policies. The new policy gives e...

We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapte...