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A graphical model for collective behavior learning using minority games

A graphical model for collective behavior learning using min...

引用

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

引用

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 lab.l 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...

引用

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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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 topic model of observing Chinese characters

A topic model of observing Chinese characters

引用

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 lab.l transfer mechanism to automatically recommend promising tags to each image by assigning each image a category lab.l 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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What is the basic semantic unit of chinese language? A computational approach based on topic models

What is the basic semantic unit of chinese language? A compu...

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12th Meeting on Mathematics of Language, MOL 12

作者： Zhao, Qi Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University Beijing China Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9783642232107

Chinese language has been generally regarded as a Subject-Verb -Object (SVO) language and the basic semantic unit is the Chinese word that is usually consisted by two or more Chinese characters. However, word-centered structure of Chinese language has been controversial in linguistics. Some recent research in computational linguistics in Chinese language suggests that the character-based models perform better than the word-based models in some applications such word segmentation. In this paper, the word-based topic models and the character-based models are tested for modeling Chinese language, respectively. By empirical studies, we demonstrated the effectiveness of using Chinese characters as the basic semantic units. These two models have close performance in text classifications while the character-based model has a better quality in language modeling and a much smaller vocabulary. By testing on a bilingual corpus, three independent topic models based on Chinese words, Chinese characters and English words are trained and compared to each other. we verify the capability of topic models in modeling semantics by experiments across Chinese and English. The classification accuracy can also be boosted up by aggregating the classification results from the three independent topic models. © 2011 Springer-Verlag.

关键词： Computational linguistics

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DIVA: Domain invariant variational autoencoder

DIVA: Domain invariant variational autoencoder

引用

2019 Deep Generative Models for Highly Structured Data, DGS@ICLR 2019 Workshop

作者： Ilse, Maximilian Tomczak, Jakub M. Louizos, Christos Welling, Max Amsterdam Machine Learning Lab. University of Amsterdam Netherlands TNO Intelligent Imaging United Kingdom CIFAR Canada

We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the domain invariant VAE (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the class, one for the domain and one for the object itself. In addition, we highlight that due to the generative nature of our model we can also incorporate unlab.led data from known or previously unseen domains. This property is highly desirable in fields like medical imaging where lab.led data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlab.lled data can boost the performance even further. © Deep Generative Models for Highly Structured Data, DGS@ICLR 2019 *** right reserved.

关键词： Medical imaging

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A k-hyperplane-based neural network for non-linear regression

A k-hyperplane-based neural network for non-linear regressio...

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IEEE International Conference on Cognitive Informatics

作者： He, Hongmei Qin, Zengchang Department of Engineering Mathematics University of Bristol Bristol United Kingdom Intelligent Computing and Machine Learning Lab. School of Automation and Electrical Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781424480401

For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperplanes for non-linear regression. A k-hyperplane clustering algorithm is developed to split the data to several clusters. The experiments are done on an artificial time series, and the convergence of k-hyperplane clustering algorithm and neural network gradient training algorithm is examined. The dimension of inputs affect the clustering performance very much. Neural network fusion can get some compensation in performance. It is shown that the prediction performance of the model for the time series is very good. The model can be further exploited for many real applications. © 2010 IEEE.

关键词： Clustering algorithms

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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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