For both single probability estimation trees (PETs) and ensembles of such trees, commonly employed class probability estimates correct the observed relative class frequencies in e...
This paper presents a data-driven approach for feature selection to address the common problem of dealing with high-dimensional data. This approach is able to handle the real-valu...
Motion estimation is known to be a non-convex optimization problem. This non-convexity comes from several ambiguities in motion estimation such as the aperture problem, or fast mo...
This paper proposes a framework for distributed sequential parameter estimation in wireless sensor networks. In the proposed scheme, the estimator is updated sequentially at the c...
Block-level sampling is far more efficient than true uniform-random sampling over a large database, but prone to significant errors if used to create database statistics. In this ...