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We propose a multiple instance learning approach to content-based retrieval of classroom video for the purpose of supporting human assessing the learning environment. The key element of our approach is a mapping between the semantic concepts of the assessment system and features of the video that can be measured using techniques from the fields of computer vision and speech analysis. We report on...
Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feed and audit trail information from market operators now make the full observation of market participants' actions possible. A key question is the extent to which it is possible to understand and characterize the behavior of individual...
We present new algorithms for inverse reinforcement learning (IRL, or inverse optimal control) in convex optimization settings. We argue that finite-space IRL can be posed as a convex quadratic program under a Bayesian inference framework with the objective of maximum a posteriori estimation. To deal with problems in large or even infinite state space, we propose a Gaussian process model and use preference...
There are many scenarios in which multi-instance learning problems may be difficult to solve because of a lack of correctly labeled examples for algorithm training. Labeled examples may be difficult or expensive to obtain because human effort is often needed to produce labels and because there may be limitations on the ability to collect large samples for training from a homogeneous population. In...
A new approach to adapt the kernel scale and orientation in real-time tracking is proposed. The iterative procedure, mean shift, is the key point to find the most credible target location. Though it performs well in some bad conditions, such as camera motion, partial occlusions, and background clutters, it has limited performance on tracking the object with the changing size. In this paper, the adaptive...
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