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18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014

作者： Khawar, Farhan Qin, Zengchang Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing China

ISBN: (纸本)9783319066042

The Minority Game (MG) is a simple game theory model for the collective behavior of agents in an idealized situation where they compete for some finite resource. In this paper, we assume that collective behavior is determined by the aggregation of individual actions of agents. This causal relation between collective behavior and individual actions is investigated. A graphical model is proposed to model the generative process of collective behavior using a group of agents whose actions are modeled by minority games. In this model, we can infer the individual behavior of the agents by training on the global information, and then make predictions about the future collective behavior. Experimental results on a set of stock indexes from the Chinese market and foreign exchange (FX) rates show that the new proposed model can effectively capture the rises and falls of market and be significantly better than a random predictor. This framework also provides a new data mining paradigm for analyzing collective data by modeling micro-level actions of agents using game theory models. © 2014 Springer International Publishing.

关键词： Data mining

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Statistical fault localization using execution sequence

Statistical fault localization using execution sequence

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2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： You, Zunwen Qin, Zengchang Zheng, Zheng Beihang University China Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University China

ISBN: (纸本)9781467314855

Fault localization is one of the most expensive and time consuming jobs in program debugging. Many approaches were proposed in order to locate faults effectively and efficiently. In this paper, we proposed a novel statistical approach by exploiting the statistical behavior of two sequentially connected predicates in the execution. If the predicates are regarded as the vertices of a graph, then the edges of the graph represent the transition of two sequential predicates in the execution trace of the program. The label of each edge is the frequency of each transition. For each edge, we apply hypothesis testing to evaluate the difference between edge evaluation bias in the passed runs and that in the failed runs. The edges are ranked according to the fault relevance score obtained from the hypothesis testing. The experimental results on Siemens suite show that the our proposed predicate-based fault localization method outperforms other well-used statistical fault localization techniques. © 2012 IEEE.

关键词： Program debugging

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Image super-resolution using local learnable kernel regression

Image super-resolution using local learnable kernel regressi...

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11th Asian Conference on Computer Vision, ACCV 2012

作者： Liao, Renjie Qin, Zengchang Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9783642374302

In this paper, we address the problem of learning-based image super-resolution and propose a novel approach called Local Learnable Kernel Regression (LLKR). The proposed model employs a local metric learning method to improve the kernel regression for reconstructing high resolution images. We formulate the learning problem as seeking multiple optimal Mahalanobis metrics to minimize the total kernel regression errors on the training images. Through learning local metrics in the space of low resolution image patches, our method is capable to build a precise data-adaptive kernel regression model in the space of high resolution patches. Since the local metrics split the whole data set into several subspaces and the training process can be executed off-line, our method is very efficient at runtime. We demonstrate that the new developed method is comparable or even outperforms other super-resolution algorithms on benchmark test images. The experimental results also show that our algorithm can still achieve a good performance even with a large magnification factor. © 2013 Springer-Verlag.

关键词： Image enhancement

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Cross-modal information retrieval - A case study on Chinese wikipedia

Cross-modal information retrieval - A case study on Chinese ...

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8th International Conference on Advanced Data Mining and Applications, ADMA 2012

作者： Cong, Yonghui Qin, Zengchang Yu, Jing Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China Department of Biomedical Engineering Rutgers University United States

ISBN: (纸本)9783642355264

Probability models have been used in cross-modalmultimedia information retrieval recently by building conjunctive models bridging the text and image components. Previous studies have shown that cross-modal information retrieval systemusing the topic correlation model (TCM) outperforms state-of-the-art models in English corpus. In this paper, we will focus on the Chinese language, which is different from western languages composed by alphabets. Words and characters will be chosen as the basic structural units of Chinese, respectively. We also set up a test database, named Ch-Wikipedia, in which documents with paired image and text are extracted fromChinese website *** investigate the problems of retrieving texts (ranked by semantic closeness) given an image query, and vice versa. The capabilities of the TCM model is verified by experiments across the Ch-Wikipedia dataset. © Springer-Verlag 2012.

关键词： Semantics

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Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

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Frontiers of Computer Science 2013年第6期7卷 852-863页

作者： Zengchang QIN Tao WAN Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China School of Biological Science and Medical Engineering Beihang University Beijing 100191 China Department of Biomedical Engineering Case Western Reserve University Cleveland OH 44106 USA

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees （PETs） give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree （LDT） is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.

关键词： fuzzy labels label semantics random set probability estimation tree mass assignment linguistic decision tree naive Bayes

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Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features

Classification of benign and malignant breast tumors in ultr...

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3rd International Conference on intelligent Human-machine Systems and Cybernetics, IHMSC 2011

作者： Liao, Renjie Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing 100191 China Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9780769544441

We establish a new set of features for differentiating benign from malignant breast lesions using ultrasound (US) images. Two types of features (sonographic and textural features) are considered. Among them, three sonographic features are novel. Sonograms of 321 pathologically proven breast cases are analyzed and classified into benign and malignant categories. The discrimination capability of the extracted features are evaluated using the support vector machines (SVM) in comparison with the results obtained from artificial neural networks (ANN) and K-nearest neighbor (KNN) classifier. The simulations demonstrate that the proposed algorithm can be an integral part to US computer-aided diagnosis (CAD) systems for breast cancer or an independent program to help accurately distinguish benign solid breast nodules from malignant nodules. © 2011 IEEE.

关键词： Computer aided diagnosis

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A robot vision system in ABU robocon contest: Visual servoing navigation and object tracking

A robot vision system in ABU robocon contest: Visual servoin...

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2nd International Conference on Materials and Products Manufacturing Technology, ICMPMT 2012

作者： Li, Xihua Zhang, Ping Lin, Sina Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Intelligent Computing and Machine Learning Lab Beihang University Beijing 100191 China

ISBN: (纸本)9783037855447

This article took the 2011 ABU Robocon Contest as background, and introduced the design and implementation of a vision system for visual servoing navigation and object tracking in this contest. For visual servoing navigation part, it had a series of simple image processing and then added to a previous control law;tests showed only 3cm distance and 1°angle error occur when robot ran one line of 6m. For object tracking part, article introduced a combined algorithm for the detecting of a black and white semi-circle object, which moved quickly and uncertainty in a colorful background and shining environment;tests showed the proposed algorithm work better than other exist algorithms in complex environment. © (2013) Trans Tech Publications, Switzerland.

关键词： Navigation

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Stable matching in structured networks 14th

Stable matching in structured networks

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14th International Workshop on Knowledge Management and Acquisition for intelligent Systems, PKAW2016

作者： Ling, Ying Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9783319427058

Stable matching studies how to pair members of two sets with the objective to achieve a matching that satisfies all participating agents based on their preferences. In this research, we consider the case of matching in a social network where agents are not fully connected. We propose the concept of D-neighbourhood associated with connective costs to investigate the matching quality in four types of well-used networks. A matching algorithm is proposed based on the classical Gale-Shapley algorithm under constraints of network topology. Through experimental studies, we find that the matching outcomes in scale-free networks yield the best average utility with least connective costs comparing to other structured networks. This research provides insights for understanding matching behavior in social networks like marriage, trade, partnership, online social and job search. © Springer International Publishing Switzerland 2016.

关键词： Costs

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A topic model of observing Chinese characters

A topic model of observing Chinese characters

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International Conference on intelligent Human-machine Systems and Cybernetics

作者： Zhang, Yunkai Qin, Zengchang College of Software Beihang University Beijing 100191 China Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University 100191 China

ISBN: (纸本)9780769541518

The Topic Models are a class of hierarchical statistical models for analyzing document collections and it has become one of the most used techniques in Natural Language Processing in the recent years. It assumes that each document could be expressed as a mixture of topics and each topic could be characterized by a distribution over words. In previous research [6], like in English language, Topic Models for Chinese Language use the words as observing data. In this research, we demonstrated the effectiveness of using Chinese characters as the basic units of observing data. The comparisons with those models based on Chinese words and English words are presented. © 2010 IEEE.

关键词： Natural language processing systems

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Semi-automatic image annotation using sparse coding

Semi-automatic image annotation using sparse coding

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2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Zhang, Weifeng Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing 100191 China School of Medicine Boston University Boston MA 02215 United States

ISBN: (纸本)9781467314855

Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. It has become a new research focus and many techniques have been proposed to solve this problem. In this paper, a novel semi-auto image annotation technique is proposed. The new developed method uses a label transfer mechanism to automatically recommend promising tags to each image by assigning each image a category label first. Since image representation is one of the key problems in image annotation, we utilize a sparse coding based spatial pyramid matching as an effective way to model and interpret image features. Experimental results demoustrate that the proposed method outperforms the current state-of-the-art methods on two benchmark image datasets. © 2012 IEEE.

关键词： Image analysis

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