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An Empirical Comparative Study on Two Large-Scale Hierarchical Text Classification Approaches

International Journal of Computer Processing of Languages 2011年第4期23卷 309-325页

作者： JIAN ZHANG HAI ZHAO LIQING ZHANG BAO-LIANG LU Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University 800 Dongchuan Rd. Shanghai 200240 China Department of Computer Science Virginia Tech Blacksburg VA 24061 USA

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. Our experimental results show that the latter outperforms the former for the LSHTC task, which is quite different from the existing results for normal text categorization tasks. In addition, this paper compares different similarity measures and ranking strategies in k -NN framework for LSHTC task. From our empirical study, conclusions can be drawn that k -NN is more appropriate for the LSHTC task than hierarchical SVM. BM25 outperforms other similarity measures and ListWeak gains a better performance than other ranking strategies. Our empirical results also indicate 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： Large-scale text classification hierarchical text classification text classification k -NN hierarchical SVM similarity measure ranking strategies

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Experimental investigations of elliptical ultrasonic assisted grinding (EUAG) of monocrystal sapphire

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International Journal of Abrasive Technology 2011年第4期4卷 279-289页

作者： Liang, Zhiqiang Wang, Xibin Wu, Yongbo Key Laboratory of Fundamental Science for Advanced Machining Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing 100081 China Department of Machine Intelligence and Systems Engineering Akita Prefectural University 84-4 Tsuchiya-ebinokuchi Yurihonjo Akita 015-0055 Japan

In our previous work, an elliptical ultrasonic assisted grinding (EUAG) method was proposed and the experimental works including the establishment of experimental apparatus and the preliminary experimental results were published earlier in this journal. In this paper, further results on experimental investigations of EUAG of monocrystal sapphire were presented;a series of grinding experiments are carried out to reveal the effects of the process parameters on the surface roughness;by examining the wheel surface conditions and the surface roughness, the influence of wheel wear mode on the surface roughness during the grinding process is also clarified. Copyright © 2011 Inderscience Enterprises Ltd.

关键词： Surface roughness

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功耗约束下的3D多核芯片芯核级测试调度算法

功耗约束下的3D多核芯片芯核级测试调度算法

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第七届中国测试学术会议

作者： Wang Wei 王伟 Lin Zhuo-wei 林卓伟 Chen Tian 陈田 Liu Jun 刘军 Fang Fang 方芳 Wu Xi 吴玺 AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine HeFei AnHui H 合肥工业大学情感计算与先进智能机器安徽省重点实验室安徽合肥230009 合肥工业大学计算机与信息学院安徽合肥230009 School of Management Hefei University of Technology HeFei 230009 合肥工业大学管理学院安徽合肥230009

三维堆叠集成电路测试中的一个关键的挑战是在功耗约束下,在绑定前测试和绑定后测试中,协同优化测试应用时间和测试硬件开销.将传统的二维芯片的绑定前和绑定后测试调度方法运用于三维堆叠集成电路的测试调度会导致测试应用时间的延长.... 详细信息

三维堆叠集成电路测试中的一个关键的挑战是在功耗约束下,在绑定前测试和绑定后测试中,协同优化测试应用时间和测试硬件开销.将传统的二维芯片的绑定前和绑定后测试调度方法运用于三维堆叠集成电路的测试调度会导致测试应用时间的延长.我们分别针对未堆叠的集成电路和N(N≥2)层芯片堆叠的3D-SICs,提出了一种功耗约束下的测试调度优化算法.在ITC'02基准电路的实验结果表明,算法在功耗约束下,测试应用时间和测试数据寄存器个数分别减少多达33.8％和28.6％,证明算法能有效地权衡测试应用时间和硬件开销.

关键词：集成电路测试测试调度优化算法功耗约束

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Flatten hierarchies for large-scale hierarchical text categorization

Flatten hierarchies for large-scale hierarchical text catego...

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International Conference on Digital Information Management

作者： Wang, Xiao-Lin Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781424475728

Hierarchies are very popular in organizing documents and web pages, hence automated hierarchical classification techniques are desired. However, the current dominant hierarchical approach of top-down method suffers accuracy decrease compared with flat classification approaches, because of error propagation and bottom nodes' data sparsity. In this paper we flatten hierarchies to relieve such accuracy decrease in top-down method, which aims to make hierarchies both effective enough to make large-scale classification tasks feasible, and simple enough to ensure high classification accuracy. We propose two flattening strategies based on these two causes of the accuracy decrease, and experimental results show that the flattening strategy designed for error propagation is more effective, which suggests that hierarchies with lots of branches at top layers can provide high classification accuracy. Besides, we analyze the computational complexity before and after flattening, which approximately agree with the experimental results. ©2010 IEEE.

关键词： Websites

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A feasibility study on elliptical ultrasonic assisted grinding of sapphire substrate

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International Journal of Abrasive Technology 2010年第3期3卷 190-202页

作者： Liang, Zhiqiang Wang, Xibin Zhao, Wenxiang Wu, Yongbo Sato, Takashi Lin, Weimin Key Laboratory of Fundamental Science for Advanced Machining Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing 100081 China Department of Machine Intelligence and Systems Engineering Akita Prefectural University 84-4 Tsuchiya-ebinokuchi Yurihonjo Akita 015-0055 Japan

An elliptical ultrasonic assisted grinding method is proposed for the machining of sapphire substrate in order to improve the machining efficiency and the work-surface quality. An elliptical ultrasonic vibrator is designed and produced by bonding a piezoelectric ceramic device (PZT) on a metal elastic body. The sapphire substrate is fixed onto the top face of the vibrator and ultrasonically vibrates in two dimensional (2D) vibration mode when the PZT is excited by two alternating current voltages with a phase difference. Grinding experiments are carried out with lateral modulation of elliptical vibration vertical to grinding speed direction. Experimental results show that grinding forces decrease significantly by 30% and grinding force ratio is reduced and surface roughness is improved by 15%, moreover surface fractures and cracks decrease obviously by using vibration compared with those of conventional grinding. These demonstrate that two-dimensional ultrasonic grinding is effective for high performance machining of sapphire substrate. © 2010 Inderscience Enterprises Ltd.

关键词： Vibrators

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KNOB particle swarm optimizer 1

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1st International Conference on Advances in Swarm intelligence, ICSI 2010

作者： Zhang, Junqi Liu, Kun Tan, Ying Key Laboratory of Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai 200092 China Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (数字)9783642134951

ISBN: (纸本)3642134947

It is not trivial to tune the swarm behavior just by parameter setting because of the randomness, complexity and dynamic involved in particle swarm optimizer (PSO). Hundreds of variants in the literature of last decade, brought various mechanism or ideas, sometimes also from outside of the traditional metaheuristics field, to tune the swarm behavior. While, in the same time, additional parameters have to be afforded. This paper proposes a new mechanism, named KNOB, to directly tune the swarm behavior through parameter setting of PSO. KNOB is defined as the first principal component of the statistical probability sequence of exploration and exploitation allocation along the search process. The using of the KNOB to tune PSO by parameter setting is realized through a statistical mapping, between the parameter set and the KNOB, learned by a radial basis function neural network (RBFNN) simulation model. In this way, KNOB provides an easy way to tune PSO directly by its parameter setting. A simple application of KNOB to promote is presented to verify the mechanism of KNOB. © 2010 Springer-Verlag.

关键词： Particle swarm optimization (PSO)

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Hedge Detection and Scope Finding by Sequence Labeling with Normalized Feature Selection 14

Hedge Detection and Scope Finding by Sequence Labeling with ...

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14th Conference on Computational Natural Language Learning, CoNLL 2010

作者： Zhang, Shaodian Zhao, Hai Zhou, Guodong Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Dept of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China School of Computer Science and Technology Soochow University China

ISBN: (纸本)1932432841

This paper presents a system which adopts a standard sequence labeling technique for hedge detection and scope finding. For the first task, hedge detection, we formulate it as a hedge labeling problem, while for the second task, we use a two-step labeling strategy, one for hedge cue labeling and the other for scope finding. In particular, various kinds of syntactic features are systemically exploited and effectively integrated using a large-scale normalized feature selection method. Evaluation on the CoNLL-2010 shared task shows that our system achieves stable and competitive results for all the closed tasks. Furthermore, post-deadline experiments show that the performance can be much further improved using a sufficient feature selection. © 2010 Association for Computational Linguistics

关键词： Wooden fences

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A comparative study on two large-scale hierarchical text classification tasks' solutions

A comparative study on two large-scale hierarchical text cla...

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International Conference on machine Learning and Cybernetics

作者： Zhang, Jian Zhao, Hai Lu, Bao-Liang Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China

ISBN: (纸本)9781424465262

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. © 2010 IEEE.

关键词： Learning algorithms

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An Empirical Study on Development Set Selection Strategy for machine Translation Learning∗ 5

An Empirical Study on Development Set Selection Strategy for...

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Joint 5th Workshop on Statistical machine Translation and MetricsMATR, WMT 2010 at the 48th Conference of the Associationfor Computational Linguistics, ACL 2010

作者： Hui, Cong Zhao, Hai Song, Yan Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China Department of Chinese Translation and Linguistics City University of Hong Kong Hong Kong

ISBN: (纸本)9781932432718

This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translating German, French and Spanish into English. Our main contribution in this work is about effective parameter tuning. We discover that there is a significant performance gap as different development sets are adopted. Finally, ten groups of development sets are used to optimize the model weights, and this does help us obtain a stable evaluation result. © 2010 Association for Computational Linguistics

关键词： machine translation

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A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on machine Learning and Cybernetics

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

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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