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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Weizhong Zhao Qing He Huifang Ma Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper presents a framework that actively selects informative documents pairs for semi-supervised document clustering. The semi-supervised document clustering algorithm is a Constrained DBSCAN (Cons-DBSCAN), which incorporates instance-level constraints to guide the clustering process in DBSCAN. By obtaining user feedbacks, our proposed active learning algorithm can get informative instance level constraints to aid clustering process. Experimental results show that Cons-DBSCAN with the proposed active learning approach can provide an appealing clustering performance.

关键词： intelligent agent Clustering algorithms Feedback Machine learning Conferences information processing Computers Data mining Learning systems

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Improving tree-to-tree translation with packed forests 09

Improving tree-to-tree translation with packed forests

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language processing of the AFNLP: Volume 2 - Volume 2

作者： Yang Liu Yajuan Lü Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781932432466

Current tree-to-tree models suffer from parsing errors as they usually use only 1-best parses for rule extraction and decoding. We instead propose a forest-based tree-to-tree model that uses packed forests. The model is based on a probabilistic synchronous tree substitution grammar (STSG), which can be learned from aligned forest pairs automatically. The decoder finds ways of decomposing trees in the source forest into elementary trees using the source projection of STSG while building target forest in parallel. Comparable to the state-of-the-art phrase-based system Moses, using packed forests in tree-to-tree translation results in a significant absolute improvement of 3.6 BLEU points over using 1-best trees.

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Joint decoding with multiple translation models 09

Joint decoding with multiple translation models

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language processing of the AFNLP: Volume 2 - Volume 2

作者： Yang Liu Haitao Mi Yang Feng Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781932432466

Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multiple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the translation hypergraphs they produce individually. Therefore, one model can share translations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint decoding achieves an absolute improvement of 1.5 BLEU points over individual decoding.

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Biological-inspired computational modeling of eye-motion control for object detection

Biological-inspired computational modeling of eye-motion con...

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Asian Control Conference

作者： Jun Miao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424454402

Eye movement plays an important role in human vision system. How to control eye or gaze movement automatically for image understanding is an interesting issue. This paper presents a progress of our research on biological-inspired computational modeling of eye-motion control for object detection in images. The model simulates the single and population cell coding mechanisms for learning visual context and controlling the eye movement. A comparative experiment with three coding systems is carried out and experimental results show the gradual-scale population coding system performs better than the other two coding systems on the average for object detection.

关键词： Computational modeling Biological control systems Object detection Humans Automatic control Biological system modeling Biological information theory Image coding Context modeling Feature extraction

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Image features optimizing for content-based image retrieval

Image features optimizing for content-based image retrieval

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IEEE International Conference on intelligent computing and intelligent Systems (ICIS)

作者： Zhiping Shi Xi Liu Qing He Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424447374;9781424447541

Developing low-dimensional semantics-sensitive features is crucial for content-based image retrieval (CBIR). In this paper, we present a method called M2CLDA (merging 2-class linear discriminant analysis) to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Moreover, when a new class is added, the new M2CLDA space can be approximated by only calculating a new discriminant vector for the new class. The features in the M2CLDA space have better semantic discrimination than those in traditional LDA space. Our experiments show that the proposed approach improves the performance of image retrieval and image classification dramatically.

关键词： Image retrieval Content based retrieval Linear discriminant analysis information retrieval Scattering Vectors Image classification Data mining Principal component analysis Helium

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A Lie group based Gaussian mixture model distance measure for multimedia comparison 09

A Lie group based Gaussian mixture model distance measure fo...

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1st International Conference on Internet Multimedia computing and Service, ICIMCS 2009

作者： Gong, Liyu Wang, Tianjiang Yu, Yan Liu, Fang Hu, Xiangen Intelligent and Distributed Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan Hubei 430074 China Department of Information and Computing Science School of Science Wuhan University of Science and Technology Wuhan Hubei 430065 China Department of Psychology Institute for Intelligent Systems University of Memphis Memphis TN 38152 United States

ISBN: (纸本)9781605588407

In this paper, we propose a novel method to measure the distance between two Gaussian Mixture Models. The proposed distance measure is based on the minimum cost that must paid to transform from one Gaussian Mixture Model into the other. We parameterize the components of a Gaussian Mixture Model which are Gaussian probability density functions (pdf) as positive definite lower triangular transformation matrices. Then we identify that Gaussian pdfs form a Lie group. Based on Lie group theory, the geodesic length can be used to measure the minimum cost that must paid to transform from one Gaussian pdf into the other. Combining geodesic length with the earth mover's distance, we propose the Lie group earth mover's distance for Gaussian Mixture Models. We test our distance measure in image retrieval. The experimental results indicate that our distance measure is more effective than other measures including the Kullback-Liebler divergence. Copyright 2009 ACM.

关键词： Probability density function

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Automatically Organize Web Text Resources with Frequent Term Tree

Automatically Organize Web Text Resources with Frequent Term...

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International Conference on Computer and information technology (CIT)

作者： Xiaofeng Wang Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Graduate University of the Chinese Academy of Sciences Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

With the expansion of the Web, automatically organizing large scale text resources, e.g. Web pages, becomes very important. Many Web sites, like Google and Yahoo, use hierarchical classification trees to organize text resources in Web. User can easily find the text resources that meet their requirements by navigating these hierarchical classification trees. Typically, the text resources in Web are manually assigned to the nodes of the hierarchical classification tree. This limits the hierarchical classification tree to organize large scale text resources. In this paper, we propose a Frequent Term Tree to improve the ability of hierarchical classification tree in organizing large scale text resources in Web. Different from the Fp-tree which is utilized to efficiently discover frequent patterns, the frequent term tree is used to organize resources with frequent pattern based classification. The frequent term tree can accurately assign text resources to each node of classification tree and improve the ability in organizing resources with the incremental classified text resources. The evaluation of the frequent term tree demonstrates that frequent term tree can effectively and efficiently organize text resources.

关键词： Classification tree analysis Large-scale systems Organizing Navigation Humans information processing Computers information technology Web pages Web sites

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Automatic adaptation of annotation standards: Chinese word segmentation and POS tagging: a case study 09

Automatic adaptation of annotation standards: Chinese word s...

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language processing of the AFNLP: Volume 1 - Volume 1

作者： Wenbin Jiang Liang Huang Qun Liu Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Google Research Charleston Rd. Mountain View CA

ISBN: (纸本)9781932432459

Manually annotated corpora are valuable but scarce resources, yet for many annotation tasks such as treebanking and sequence lab.ling there exist multiple corpora with different and incompatible annotation guidelines or standards. This seems to be a great waste of human efforts, and it would be nice to automatically adapt one annotation standard to another. We present a simple yet effective strategy that transfers knowledge from a differently annotated corpus to the corpus with desired annotation. We test the efficacy of this method in the context of Chinese word segmentation and part-of-speech tagging, where no segmentation and POS tagging standards are widely accepted due to the lack of morphology in Chinese. Experiments show that adaptation from the much larger People's Daily corpus to the smaller but more popular Penn Chinese Treebank results in significant improvements in both segmentation and tagging accuracies (with error reductions of 30.2% and 14%, respectively), which in turn helps improve Chinese parsing accuracy.

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Inductive transfer learning for unlab.led target-domain via hybrid regularization

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Chinese Science Bulletin 2009年第11期54卷 2470-2478页

作者： ZHUANG FuZhen LUO Ping HE Qing SHI ZhongZhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Hewlett Packard Labs China Beijing 100084 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

Recent years have witnessed an increasing interest in transfer learning. This paper deals with the classification problem that the target-domain with a different distribution from the source-domain is totally unlab.led, and aims to build an inductive model for unseen data. Firstly, we analyze the problem of class ratio drift in the previous work of transductive transfer learning, and propose to use a normalization method to move towards the desired class ratio. Furthermore, we develop a hybrid regularization framework for inductive transfer learning. It considers three factors, including the distribution geometry of the target-domain by manifold regularization, the entropy value of prediction probability by entropy regularization, and the class prior by expectation regularization. This framework is used to adapt the inductive model learnt from the source-domain to the target-domain. Finally, the experiments on the real-world text data show the effectiveness of our inductive method of transfer learning. Meanwhile, it can handle unseen test points.

关键词：归纳学习正规化标签杂交归一化法预测概率文字资料归纳法

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Automatic sports genre categorization and view-type classification over large-scale dataset 09

Automatic sports genre categorization and view-type classifi...

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17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Li, Lingfang Zhang, Ning Duan, Ling-Yu Huang, Qingming Du, Jun Guan, Ling Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Ryerson Multimedia Research Laboratory Ryerson University Toronto ON Canada Institute of Digital Media Peking University Beijing 100871 China Graduate University of Chinese Academy of Sciences Beijing 100190 China NEC Research Labs. China Beijing 100084 China

ISBN: (纸本)9781605586083

This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications. Copyright 2009 ACM.

关键词： Support vector machines

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