We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
We present an integrated framework for learning asymmetric boosted classifiers and online learning to address the problem of online learning asymmetric boosted classifiers, which ...
This paper presents a unified approach to colour image coding, content-based indexing, and retrieval for database applications. The binary space partitioning (BSP) tree, tradition...
We propose a new fibre tracking algorithm for cardiac DTMRI that parts with the locally "greedy" paradigm intrinsic to conventional tracking algorithms. We formulate the...
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...