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检索条件"机构=Intelligent Computing & Machine Learning Lab"

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

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

引用

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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Semi-automatic image annotation using sparse coding

Semi-automatic image annotation using sparse coding

引用

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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An efficient minimum vocabulary construction algorithm for language modeling 12

引用

25th International Conference on Industrial Engineering and Other Applications of Applied intelligent Systems, IEA/AIE 2012

作者： Lin, Sina Qin, Zengchang Huang, Zehua Wan, Tao Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing China School of Advanced Engineering Beihang University China Robotics Institute Carnegie Mellon University Pittsburgh United States School of Medicine Boston University Boston United States

ISBN: (纸本)9783642310867

In learning a new word by a dictionary, we first need to know a set of "basic words" which are frequently appeared in word definitions. It often happens that you cannot understand the word you looked up because there are still some words you do not understand in its definitions or explanations provided by the dictionary. You can keep looking up these new words recursively till they all can be well explained by some basic words you already knew. How to automatically find a minimum set of such basic words to define (or recursively define) the entire vocabulary in a given dictionary is what are going to discuss in this paper. We propose an efficient algorithm to construct the Minimum Vocabulary (MV) using the word frequency information. The minimum vocabulary can be used for language modeling and experimental results demonstrate the effectiveness of using the minimum vocabulary as features in text classification. © 2012 Springer-Verlag.

关键词： Classification (of information)

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

引用

7th IFIP International Conference on intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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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...

引用

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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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...

引用

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

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

引用

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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An application of compressive sensing for image fusion 10

An application of compressive sensing for image fusion

引用

ACM International Conference on Image and Video Retrieval, ACM-CIVR 2010

作者： Wan, Tao Qin, Zengchang Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1UB United Kingdom Intelligent Computing and Machine Learning Lab. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781450301176

Compressive sensing(CS) has inspired significant interest because of its compressive capability and lack of complexity on the sensor side. In this paper, we present a study of three sampling patterns and investigate their performance on CS reconstruction. We then propose a new image fusion algorithm in the compressive domain by using an improved sampling pattern. There are few studies regarding the applicability of CS to image fusion. The main purpose of this work is to explore the properties of compressive measurements through different sampling patterns and their potential use in image fusion. The study demonstrates that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the multiresolution (MR) domain. The simulations show that the proposed CS-based image fusion algorithm provides promising results. Copyright © 2010 ACM.

关键词： Compressed sensing

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