Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Let H be a graph, and let CH(G) be the number of (subgraph isomorphic) copies of H contained in a graph G. We investigate the fundamental problem of estimating CH(G). Previous res...
We present PROUD - A PRObabilistic approach to processing similarity queries over Uncertain Data streams, where the data streams here are mainly time series streams. In contrast t...
Mi-Yen Yeh, Kun-Lung Wu, Philip S. Yu, Ming-Syan C...
— Accurate needle insertion in 3D environment is always a grand challenge. When multiple targets are located in the tissue, a procedure of inserting multiple needles from a singl...
Jijie Xu, Vincent Duindam, Ron Alterovitz, Jean Po...
We study the randomized version of a computation model (introduced in [9, 10]) that restricts random access to external memory and internal memory space. Essentially, this model c...