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International International Symposium on Computer and Information Sciences (ISCIS)

作者： Yang Yang Yixu Song Muhammad Akram Shaikh Jiaxin Wang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and TechnologyDepartment of Computer Science Tsinghua University Beijing China

High-precision localization is one of the important applications in the field of computer vision. In this paper a high-precision template localization algorithm based on SIFT (scale invariant feature transform) is presented. The proposed method is composed of three main steps. In the initial step the SIFT features are extracted. With these features the basic matching strategy and clustering method similar distance threshold (SDT) are investigated to match the keypoints between template and test images and eliminate the possibility of mismatch. Then iterative least square method (ILSM) is adopted to locate the template and improve the accuracy. Compared with the traditional template matching methods, the proposed method could enhance the robustness effectively, which ensures to give correct results, no matter the test image changes its scale, rotates itself or is covered partly. The localization accuracy reaches 0.1 pixels.

关键词： Testing Feature extraction Image matching Application software Computer vision Robustness Bonding Laboratories Clustering algorithms Clustering methods

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Text Classification Based on Transfer Learning and Self-Training

Text Classification Based on Transfer Learning and Self-Trai...

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International Conference on Natural Computation (ICNC)

作者： Yabin Zheng Shaohua Teng Zhiyuan Liu Maosong Sun State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Traditional text classification methods make a basic assumption: the training and test set are homologous, while this naive assumption may not hold in the real world, especially in the Web environment. Documents on the Web change from time to time, pre-trained model may be out of date when applied to new emerging documents. However some information of training set is nonetheless useful. In this paper we proposed a novel method to discover the constant common knowledge in both training and test set by transfer learning, then a model is built based on this knowledge to fit the distribution in test set. The model is reinforced iteratively by adding most confident instances in unlabeled test set to training set until convergence, which is a self-training process, preliminary experiment shows that our method achieves an approximately 8.92% improvement as compared to the standard supervised-learning method.

关键词： Text categorization Convergence Machine learning Automatic testing Support vector machines Text mining Sun intelligent systems Information science Computer science

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COX-2 activity prediction in Chinese medicine using neural network based ensemble learning methods

COX-2 activity prediction in Chinese medicine using neural n...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wei Li Yannan Zhao Yixu Song Zehong Yang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science Tsinghua University Beijing China

In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine quantitative structure-activity relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting, are fast and effective ways in the activity prediction of Chinese medicine QSAR research, which is generally based on a small amount of training samples.

关键词： Artificial neural networks Boosting Inhibitors Training Bagging Learning systems Support vector machines

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Scene understanding with discriminative structured prediction

Scene understanding with discriminative structured predictio...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jinhui Yuan Jianmin Li Bo Zhang State Key Laboratory on Intelligent Technology and Systems China Tsinghua National Laboratory for Information Science and Technology China Department of Computer Science and Technology Tsinghua University China

Spatial priors play crucial roles in many high-level vision tasks, e.g. scene understanding. Usually, learning spatial priors relies on training a structured output model. In this paper, two special cases of discriminative structured output model, i.e. conditional random fields (CRFs) and max-margin Markov networks (M 3 N), are demonstrated to perform image scene understanding. The two models are empirically compared in a fair manner, i.e. using the common feature representation and the same optimization algorithm. Particularly, we adopt online exponentiated gradient (EG) algorithm to solve the convex duals of both models. We describe the general procedure of EG algorithm and present a two-stage training procedure to overcome the degeneration of EG when exact inference is intractable. Experiments on a large scale image region annotation task are carried out. The results show that both models yield encouraging results but CRFs slightly outperforms M 3 N.

关键词： Layout Inference algorithms Markov random fields Laboratories Computer vision intelligent systems intelligent structures Information science Computer science Large-scale systems

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Identifying Opinion Leaders in BBS

Identifying Opinion Leaders in BBS

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Zhongwu Zhai Hua Xu Peifa Jia State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology CS&T Department Tsinghua University Beijing China

Opinion leaders play a very important role in information diffusion; they are found in all fields of society and influence the opinions of the masses in their fields. Most proposed algorithms on identifying opinion leaders in Internet social network are global measure algorithms and usually omit the fact that opinion leaders are field-limited. We propose and test several algorithms, including interest-field based algorithms and global measure algorithms, to identify opinion leaders in BBS. Our experiments show that different algorithms are sensitive to different indicators; the interest-field based algorithms which not only take into account of the social networkspsila structure but also the userspsila interest space are more reasonable and effective in identifying opinion leaders in BBS. The interest-field based algorithms are sensitive to the high status nodes in the social network, and their performance relies on the quality of field discovery.

关键词： Social network services intelligent agent Laboratories intelligent systems Information science Societies IP networks Testing Java Blogs

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Adaptive FIR Filtering under Minimum Error/Input Information Criterion

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IFAC Proceedings Volumes 2008年第2期41卷 3539-3543页

作者： Badong Chen Jinchun Hu Hongbo Li Zengqi Sun State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 P.R. China (Tel: 86-10-62777704

In this paper, we use the mutual information between error/input as the cost function for adaptive filtering. For the finite-impulse response (FIR) filter, the connections between the minimum error/input information (MEII) criterion and traditional mean-square error (MSE) criterion are investigated. We show that, for Gaussian case, the MEII criterion is equivalent to the well-known orthogonality condition. Based on the MEII criterion and kernel density estimation, we derive a stochastic gradient algorithm. Simulation results emphasize the effectiveness of this new algorithm.

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A simple state feedback controller design method of networked control systems with time delay and packet dropout

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IFAC Proceedings Volumes 2008年第2期41卷 12575-12580页

作者： Hongbo Li Zengqi Sun Mo-Yuen Chow Badong Chen Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China 100084 Department of Electrical and Computer Engineering North Carolina State University Raleigh NC 27695-7911 USA

This paper presents a simple yet effective method to design state feedback controller for networked control systems (NCSs). By introducing the lifting technique into NCSs and by considering the balance between effectiveness and simplicity, a novel discrete-time switch model is proposed with the consideration of time delay and packet dropout during the transmission of packets. In terms of the given model, we give sufficient conditions for the existence of state feedback controller such that the closed-loop NCSs are asymptotically stable. Based on the obtained stability conditions, a homotopy-based iterative LMI algorithm is developed to get the state feedback gain. Simulation and experimental results are given to demonstrate the effectiveness of the proposed approaches.

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state feedback controller design of networked control systems with time delay and packet dropout

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IFAC Proceedings Volumes 2008年第2期41卷 6626-6631页

In this paper, the stability analysis and synthesis problems for networked control systems (NCSs) are investigated. By introducing the lifting technique into NCSs, a novel discrete-time switch model is proposed with the consideration of time delay and packet dropout during the transmission of packets. It describes NCSs as a switch system, and therefore enables us to apply the theory from switch systems to study NCSs in discrete-time domain. In terms of the given model, we give sufficient conditions for the existence of state feedback controller such that the closed-loop NCSs are asymptotically stable. Based on the obtained stability conditions, a homotopy-based iterative LMI algorithm is developed to get the state feedback gain. Simulation results are given to demonstrate the effectiveness of the proposed approaches.

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An Automatic Online News Topic keyphrase Extraction System

An Automatic Online News Topic Keyphrase Extraction System

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Canhui Wang Min Zhang Liyun Ru Shaoping Ma State Key Lab of Intelligent technology & systems Tsinghua National Laboratory for Information Science and Technology CS&T Department Tsinghua University Beijing China

News Topics are related to a set of keywords or keyphrases. Topic keyphrases briefly describe the key content of topics and help users decide whether to do further reading about them. Moreover, keyphrases of a news topic can be considered as a cluster of related terms, which provides term relationship information that can be integrated into information retrieval models. In this paper, an automatic online news topic keyphrase extraction system is proposed. News stories are organized into topics. keyword candidates are firstly extracted from single news stories and filtered with topic information. Then a phrase identification process combines keywords into phrases using position information. Finally, the phrases are ranked and top ones are selected as topic keyphrases. Experiments performed on practical Web datasets show that the proposed system works effectively, with a performance of precision=70.61% and recall=67.94%.

关键词： Data mining intelligent agent Information retrieval Search engines intelligent systems Laboratories Information science Information filtering Information filters Indexing

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A weighted subspace approach for improving bagging performance

A weighted subspace approach for improving bagging performan...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qu-Tang Cai Chun-Yi Peng Chang-Shui Zhang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China Microsoft Research Asia Beijing China

Bagging is an ensemble method that uses random resampling of a dataset to construct models. In classification scenarios, the random resampling procedure in bagging induces some classification margin over the dataset. In addition, when perform bagging in different feature subspaces, the resulting classification margins are likely to be diverse. We take into account the diversity of classification margins in feature sub- spaces for improving the performance of bagging. We first study the average error rate of bagging, convert our task into an optimization problem for determining some weights for feature subspaces, and then assign the weights to the sub- spaces via a randomized technique in classifier construction. Experimental results demonstrate that our method is able to further improve the classification accuracy of bagging, and also outperforms several other ensemble methods including AdaBoost, random forests and random subspace method.

关键词： Bagging Laboratories Diversity reception Error analysis Voting intelligent systems Information science Automation Asia Optimization methods

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