Institution
University of Texas at Arlington
Education•Arlington, Texas, United States•
About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.
Papers published on a yearly basis
Papers
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TL;DR: An integral reinforcement learning algorithm on an actor-critic structure is developed to learn online the solution to the Hamilton-Jacobi-Bellman equation for partially-unknown constrained-input systems and it is shown that using this technique, an easy-to-check condition on the richness of the recorded data is sufficient to guarantee convergence to a near-optimal control law.
410 citations
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TL;DR: In this paper, the mediating effects of salesperson information communication behaviors between social media use and customer satisfaction were investigated using salesperson-reported data, within a B2B context, empirically test a model using structural equation modeling.
409 citations
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25 Jan 2015TL;DR: A novel large-scale multi-view spectral clustering approach based on the bipartite graph that uses local manifold fusion to integrate heterogeneous features and can be easily extended to handle the out-of-sample problem.
Abstract: In this paper, we address the problem of large-scale multi-view spectral clustering. In many real-world applications, data can be represented in various heterogeneous features or views. Different views often provide different aspects of information that are complementary to each other. Several previous methods of clustering have demonstrated that better accuracy can be achieved using integrated information of all the views than just using each view individually. One important class of such methods is multi-view spectral clustering, which is based on graph Laplacian. However, existing methods are not applicable to large-scale problem for their high computational complexity. To this end, we propose a novel large-scale multi-view spectral clustering approach based on the bipartite graph. Our method uses local manifold fusion to integrate heterogeneous features. To improve efficiency, we approximate the similarity graphs using bipartite graphs. Furthermore, we show that our method can be easily extended to handle the out-of-sample problem. Extensive experimental results on five benchmark datasets demonstrate the effectiveness and efficiency of the proposed method, where our method runs up to nearly 3000 times faster than the state-of-the-art methods.
407 citations
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01 Feb 2011TL;DR: It is shown that, similar to Q-learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control.
Abstract: Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q-learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.
406 citations
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TL;DR: A new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking and a semi- supervised long-term RF algorithm to refine the multimedia data representation.
Abstract: We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.
405 citations
Authors
Showing all 11918 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
David H. Adams | 155 | 1613 | 117783 |
Andrew White | 149 | 1494 | 113874 |
Kaushik De | 139 | 1625 | 102058 |
Steven F. Maier | 134 | 588 | 60382 |
Andrew Brandt | 132 | 1246 | 94676 |
Amir Farbin | 131 | 1125 | 83388 |
Evangelos Gazis | 131 | 1147 | 84159 |
Lee Sawyer | 130 | 1340 | 88419 |
Fernando Barreiro | 130 | 1082 | 83413 |
Stavros Maltezos | 129 | 943 | 79654 |
Elizabeth Gallas | 129 | 1157 | 85027 |
Francois Vazeille | 129 | 952 | 79800 |
Sotirios Vlachos | 128 | 789 | 77317 |