We use concepts from chaos theory in order to model
nonlinear dynamical systems that exhibit deterministic behavior.
Observed time series from such a system can be embedded
into...
This paper presents a new methodology for evaluating the quality of motion estimation and stereo correspondence algorithms. Motivated by applications such as novel view generation...
Predictive state representations (PSRs) are a recently proposed way of modeling controlled dynamical systems. PSR-based models use predictions of observable outcomes of tests that...
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for mem...
M. F. Sakr, Steven P. Levitan, Donald M. Chiarulli...