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- Thomas Takeo Osugi, Kun Deng, Stephen D. Scott
- Fifth IEEE International Conference on Data…
- 2005

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We… (More)

- Kun Deng, Prashant G. Mehta, Sean P. Meyn
- IEEE Trans. Automat. Contr.
- 2011

—This paper is concerned with model reduction for complex Markov chain models. The Kullback–Leibler divergence rate is employed as a metric to measure the difference between the Markov model and its approximation. For a certain relaxation of the bi-partition model reduction problem, the solution is shown to be characterized by an associated eigenvalue… (More)

- Matt Culver, Kun Deng, Stephen D. Scott
- Sixth International Conference on Data Mining…
- 2006

In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active… (More)

- Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran
- Machine Learning
- 2008

Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously learned classifier to work under new conditions. We study the problem of reoptimizing a multi-class classifier based on its ROC hypersurface and a matrix describing the costs of each type of prediction error. For a binary… (More)

- Kun Deng, Prabir Barooah, Prashant G. Mehta
- ACC
- 2012

- Shou-Jia Sun, Tao Su, Kun Deng, Bian Wu, Chang-Hong Liang
- IEEE Transactions on Microwave Theory and…
- 2013

In this paper, a short-ended stepped-impedance dual-resonance resonator is presented. Analysis of its resonance characteristics is carried out, from which the design graph is given. By controlling the impedance and length ratios of the resonator, for the first time, the first three pairs of resonant modes corresponding to three passbands within a single… (More)

— This paper is concerned with an information-theoretic framework to aggregate a large-scale Markov chain to obtain a reduced order Markov model. The Kullback-Leibler (K-L) divergence rate is employed as a metric to measure the distance between two stationary Markov chains. Model reduction is obtained by considering an optimization problem w.r.t. this… (More)

- Yaling Zheng, Stephen D. Scott, Kun Deng
- 2010 IEEE International Conference on Data Mining
- 2010

In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, and that this labeler is noise-free. In reality, it is possible that there are multiple labelers available (such as human labelers in the online annotation tool Amazon Mechanical… (More)

- Joseph S. Niedbalski, Kun Deng, Prashant G. Mehta, Sean Meyn
- 2008

— This paper is concerned with model reduction for a complex Markov chain using state aggregation. The work is motivated in part by the need for reduced order estimation of occupancy in a building during evacuation. We propose and compare two distinct model reduction techniques, each of which is based on the potential matrix for the Markov semigroup. The… (More)

- Kun Deng, Chris Bourke, Stephen D. Scott, Julie Sunderman, Yaling Zheng
- Seventh IEEE International Conference on Data…
- 2007

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples' labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in… (More)