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International Conference on machine Learning and Cybernetics (ICMLC)

作者： Jian Zhang Hai Zhao Bao-Liang Lu Center of Brain-Like Computing and Machine Intelligence(ÇïêDepartment of Computer Science and Engineering Shanghai Jiaotong University Shanghai China MOE-Microsoft Key Laboratory of Intelligent Computing and Intelligent Systems Shanghai Jiaotong University Shanghai China

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely hierarchical support vector machine (SVM) and k nearest neighbor (k-NN) that are applied to a LSHTC task. Experiment results show that the latter outperforms the former in this LSHTC task, which is quite different from the usual results for normal text categorization tasks. Then this paper does a comparative study on different similarity measures and ranking approaches in k-NN framework for LSHTC task. Conclusions can be drawn that k-NN is more appropriate for the LSHTC task than hierarchical SVM and for a specific LSHTC task. BM25 outperforms other similarity measures and List Weak gains a better performance than other ranking approaches. We also find an interesting phenomenon that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors.

关键词： Patents Support vector machines Text categorization Nearest neighbor searches Training Classification algorithms Taxonomy

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Journal of Harbin Institute of Technology(New Series) 2009年第2期16卷 223-226页

作者：祝世虎封举富 Key Laboratory of Machine Perception(Peking University) MOE Department of Machine IntelligenceSchool of Electronics Engineering and Computer SciencePeking University

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted *** this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods *** is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP *** other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP *** on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

关键词： similarity measurement local binary pattern Earth Mover＇s Distance IMage Euclidean Distance

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Robotic Manipulation with Large Time Delay on Visual Feedback Systems

Robotic Manipulation with Large Time Delay on Visual Feedbac...

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2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2010)

作者： Takahiro Inoue Shinichi Hirai Faculty of Department of Systems Engineering of Sports Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama Japan Laboratory of Integrated Machine Intelligence and Faculty of Department of Robotics Ritsumeikan University Japan

A difference between the human and robots from the viewpoint of motor control is a sampling time in everyday movements. That is, the sampling time for controlling conventional robot systems is required to become approximately 1 ms. On the other hand, the motor control based on human nervous system permits considerably large time-delay due to the transmission latency on afferent/efferent pathways of the central nervous system. To date, it has been difficult for the robot to acquire dexterous tasks and precise movements as long as the robot system has an unexpected large delay in terms of sensory informations. Based on the above observation, this paper provides a new control strategy to accomplish precise orientation control of a target object grasped by a robotic hand consisting of two degrees-of-freedom (DOFs). The controller, named as serial two-phased (STP) controller, proposed in this study can realize secure grasping and manipulation in the case that a large visual feedback delay induced by the low specification of a camera is hiding in the control loop. Finally, through several simulations, we indicate that the closed loop dynamics designed by integrating the STP controller and a soft-fingertip structure is robust even in at most 100 ms-delay relating to the updating of camera images.

关键词： Robots Delay Trajectory Joints Visualization Muscles Humans

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ProbSim-annotation: A novel image annotation algorithm using a probability-based similarity measure

ProbSim-annotation: A novel image annotation algorithm using...

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20th Midwest Artificial intelligence and Cognitive science Conference, MAICS 2009

作者： Fang, Chunsheng Ralescu, Anca Machine Learning and Computational Intelligence Laboratory Department of Computer Science ML 0030 University of Cincinnati Cincinnati OH 45220 United States

ProbSim-Annotation is an image annotation algorithm for heterogeneous data driven by a probability-based similarity assessment. Image annotation consists of associating to an image a description in terms of labels (or words) from a dictionary. This association rests on the premise that similar images have similar annotations. Evaluation of an annotation algorithm is conveyed by the relevance of retrieval from an image data base when the query is described by labels of the dictionary. Previous studies have shown the probability-based similarity to be very useful in assessing similarity between heterogeneous data by mapping heterogeneous distances into their probability distributions. These can then be estimated from the training set. In this paper, for practical use, the empirical CDF of distances is approximated by polynomial series. Combining probability-based similarity across multiple attributes/dimensions leads to an overall similarity. This serves as an important cue to transfer annotations from the training set to a test set using a kNN algorithm. Experimental results performed on Corel5K benchmark dataset show that ProbSim-Annotation is a promising image annotation algorithm.

关键词： Image annotation

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A hierarchical artificial immune model for virus detection

A hierarchical artificial immune model for virus detection

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2009 International Conference on Computational intelligence and Security, CIS 2009

作者： Wang, Wei Zhang, Pengtao Tan, Ying He, Xingui Key Laboratory of Machine Perception Ministry of Eduction Peking University China Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (纸本)9780769539317

As viruses become more complex, existing antivirus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. Inspired by immune system, a hierarchical artificial immune system (AIS) model, which is based on matching in three layers, is proposed to detect a variety of forms of viruses. In the bottom layer, a non-stochastic but guided candidate virus gene library is generated by statistical information of viral key codes. Then a detecting virus gene library is upgraded from the candidate virus gene library using negative selection. In the middle layer, a novel storage method is used to keep a potential relevance between different signatures on the individual level, by which the mutual cooperative information of each instruction in a virus program can be collected. In the top layer, an overall matching process can reduce the information loss considerably. Experimental results indicate that the proposed model can recognize obfuscated viruses efficiently with an averaged recognition rate of 94%, including new variants of viruses and unknown viruses.

关键词： Stochastic systems

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A virus detection system based on artificial immune system

A virus detection system based on artificial immune system

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2009 International Conference on Computational intelligence and Security, CIS 2009

作者： Chao, Rui Tan, Ying Key Laboratory of Machine Perception Ministry of Eduction Peking University Beijing 100871 China Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (纸本)9780769539317

A virus detection system (VDS) based on artificial immune system (AIS) is proposed in this paper. VDS at first generates the detector set from virus files in the dataset, negative selection and clonal selection are applied to the detector set to eliminate autoimmunity detectors and increase the diversity of the detector set in the non-self space respectively. Two novel hybrid distances called hamming-max and shift r bit-continuous distance are proposed to calculate the affinity vectors of each file using the detector set. The affinity vectors of the training set and the testing set are used to train and test classifiers respectively. VDS compares the detection rates using three classifiers, knearest neighbor (KNN), RBF networks and SVM when the length of detectors is 32 - bit and 64 - bit. The experimental results show that the proposed VDS has a strong detection ability and good generalization performance. © 2009 IEEE.

关键词： Immune system

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A Framework for Multi-devices Based Intelligent Interactions

A Framework for Multi-devices Based Intelligent Interactions

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International Workshop on Intelligent Networks and Intelligent Systems (ICINIS)

作者： Chao Dong Ji-Zhi Tan Heng Wang Guo-Ping Wang Human Computer Interaction and Multimedia Laboratory Key Laboratory of Machine Perception and Intelligence MOE Department of Computer and Science Peking University Beijing China

Nowadays, intelligent interaction and multi-devices interaction have attracted great attention from researchers and industry, yet there are still no framework combining these two interactions together. We study this problem and present a feasible framework to solve it. This framework is based on multi-devices, and it combines interaction-data collecting, data managing and user modeling together. Furthermore, it provides flexible interfaces for application development and proposes an algorithm for integrating group model, individual model and feedback model. We developed several demos based on this framework, which demonstrate its effectiveness empirically.

关键词： Feedback Intelligent networks machine intelligence Personal digital assistants Application software Mobile handsets Collaborative work Intelligent systems Chaos Multimedia systems

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GPU-based parallel particle swarm optimization

GPU-based parallel particle swarm optimization

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Congress on Evolutionary Computation

作者： You Zhou Ying Tan Key Laboratory of Machine Perception and Intelligence Peking University Department of Machine Intelligence School of Electronics Engineering and Computer Science Ministry of Education Beijing China

A novel parallel approach to run standard particle swarm optimization (SPSO) on Graphic Processing Unit (GPU) is presented in this paper. By using the general-purpose computing ability of GPU and based on the software platform of Compute Unified Device Architecture (CUDA) from NVIDIA, SPSO can be executed in parallel on GPU. Experiments are conducted by running SPSO both on GPU and CPU, respectively, to optimize four benchmark test functions. The running time of the SPSO based on GPU (GPU-SPSO) is greatly shortened compared to that of the SPSO on CPU (CPU-SPSO). Running speed of GPU-SPSO can be more than 11 times as fast as that of CPU-SPSO, with the same performance, compared to CPU-SPSO, GPU-SPSO shows special speed advantages on large swarm population applications and high dimensional problems, which can be widely used in real optimizing problems.

关键词： Particle swarm optimization Central Processing Unit Graphics Concurrent computing computer architecture Ant colony optimization Application software machine intelligence Programming profession Benchmark testing

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A Hierarchical Artificial Immune Model for Virus Detection

A Hierarchical Artificial Immune Model for Virus Detection

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International Conference on Computational intelligence and Security

作者： Wei Wang Pengtao Zhang Ying Tan Xingui He Key Laboratory of Machine Perception Department of Machine Intelligence School of Electronics Engineering and Computer Science Ministry of Education Peking University Beijing China China

ISBN: (纸本)9781424454112

关键词： Immune system Libraries Detectors computer viruses Artificial immune systems Organisms Computational intelligence Security Helium

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Learning IMED via shift-invariant transformation

Learning IMED via shift-invariant transformation

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Bing Sun Jufu Feng Liwei Wang Key Laboratory of Machine Perception (Peking University) MOE Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

The IMage Euclidean Distance (IMED) is a class of image metrics, in which the spatial relationship between pixels is taken into consideration. It was shown that calculating the IMED of two images is equivalent to performing a linear transformation called Standardizing Transform (ST) and then followed by the traditional Euclidean distance. However, while the IMED is invariant to image shift, the ST is not a Shift-Invariant (SI) filter. This left as an open problem whether IMED is equivalent to SI transformation plus traditional Euclidean distance. In this paper, we give a positive answer to this open problem. Specifically, for a wider class of metrics, including IMED, we construct closed-form SI transforms. Based on the SI metric-transform connection, we next develop an image metric learning algorithm by learning a metric filter in the transform domain. This is different from all previous metric approaches. Experimental results on benchmark datasets demonstrate that the learned image metric has promising performances.

关键词： Euclidean distance Filters Pixel Support vector machines Support vector machine classification Linear discriminant analysis machine learning Sun Laboratories machine intelligence

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