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20th International Conference on Neural Information processing, ICONIP 2013

作者： Wu, Peng Duan, Fuqing Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan 250022 China

ISBN: (纸本)9783642420535

Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to choose the proper kernels in practical applications. In this paper, we propose a MKL method based on minimal redundant maximal relevance criterion and kernel alignment. The main feature of this method compared to others in the literature is that the selection of kernels is considered as a feature selection issue in the Hilbert space, and can obtain a set of base kernels with the highest relevance to the target task and the minimal redundancies among themselves. Experimental results on several benchmark classification data sets show that our proposed method can enhance the performance of MKL. © Springer-Verlag 2013.

关键词： Benchmarking

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Concept learning for cross-domain text classification: A general probabilistic framework

Concept learning for cross-domain text classification: A gen...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Zhuang, Fuzhen Luo, Ping Yin, Peifeng He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Hewlett Packard Labs China Pennsylvania State University United States

ISBN: (纸本)9781577356332

Cross-domain learning targets at leveraging the knowledge from source domains to train accurate models for the test data from target domains with different but related data distributions. To tackle the challenge of data distribution difference in terms of raw features, previous works proposed to mine high-level concepts (e.g., word clusters) across data domains, which shows to be more appropriate for classification. However, all these works assume that the same set of concepts are shared in the source and target domains in spite that some distinct concepts may exist only in one of the data domains. Thus, we need a general framework, which can incorporate both shared and distinct concepts, for cross-domain classification. To this end, we develop a probabilistic model, by which both the shared and distinct concepts can be learned by the EM process which optimizes the data likelihood. To validate the effectiveness of this model we intentionally construct the classification tasks where the distinct concepts exist in the data domains. The systematic experiments demonstrate the superiority of our model over all compared baselines, especially on those much more challenging tasks.

关键词： Classification (of information)

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A motivational system for mind model CAM

A motivational system for mind model CAM

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2013 AAAI Fall Symposium

作者： Shi, Zhongzhi Zhang, Jianhua Yue, Jinpeng Qi, Baoyuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781577356417

A motivation model is proposed in the paper. Based on the model we develop a motivational system for mind model CAM. Through the application in automatic navigation of animal robots shows the motivation system is useful to solve the dynamic and complexity automatic problem. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Motivation

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Shared structure learning for multiple tasks with multiple views

Shared structure learning for multiple tasks with multiple v...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013

作者： Jin, Xin Zhuang, Fuzhen Wang, Shuhui He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9783642409905

Real-world problems usually exhibit dual-heterogeneity, i.e., every task in the problem has features from multiple views, and multiple tasks are related with each other through one or more shared views. To solve these multi-task problems with multiple views, we propose a shared structure learning framework, which can learn shared predictive structures on common views from multiple related tasks, and use the consistency among different views to improve the performance. An alternating optimization algorithm is derived to solve the proposed framework. Moreover, the computation load can be dealt with locally in each task during the optimization, through only sharing some statistics, which significantly reduces the time complexity and space complexity. Experimental studies on four real-world data sets demonstrate that our framework significantly outperforms the state-of-the-art baselines. © 2013 Springer-Verlag.

关键词： Learning systems

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Phrase filtering for content words in hierarchical phrase-based model

Phrase filtering for content words in hierarchical phrase-ba...

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14th Workshop on Chinese Lexical Semantics, CLSW 2013

作者： Wang, Xing Xie, Jun Song, Linfeng Lv, Yajuan Yao, Jianmin School of Computer Science AndTechnology Soochow University Suzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642451843

When hierarchical phrase-based statistical machine translation systems are used for language translation, sometimes the translations' content words were lost: source-side content words is empty when translated into target texts during decoding. Although the translations' BLEU score is very high, it is difficult to understand the translations because of the loss of the content words. In this paper, we propose a basic and efficient method for phrase filtering, with which the phrase' content words translation are checked to decide whether to use the phrase in decoding or not. The experimental results show that the proposed method alleviates the problem of the loss content words' and improves the BLEU scores. © 2013 Springer-Verlag.

关键词： Decoding

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Attribute reduction based on approximation dependency degree

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Journal of Computers (Finland) 2013年第4期8卷 920-928页

作者： Li, Min Deng, ShaoBo Feng, Shengzhong Fan, Jianping NanChang Institute of Technology Nanchang Jiangxi 330099 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong 518055 China Key laboratory of intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Attribute reduction is one of the core research content of Rough sets theory. Many existing algorithms mainly are aimed at the reduction of consistency decision table, and very little work has been done for attribute reduction aimed at inconsistency decision table. In fact, the methods finding Pawlak reduction from consistent decision table are not suitable for inconsistency decision table. In this paper, we introduce the approximation dependency reduction modal and present the Quick Attribution Reduction based on Approximation Dependency Degree (Quick-ARADD), which can retain the original boundary region and the original positive region unchanged, and keep the approximation accuracy unchanged for all decision equivalence classes (the partition of universe on decision attributes) of a decision table. Theoretical analysis and experimental results show that the Quick-ARADD algorithm is effective and feasible. © 2013 ACADEMY PUBLISHER.

关键词： Decision tables

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Semantic separator learning and its applications in unsupervised Chinese text parsing

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Frontiers of Computer Science 2013年第1期7卷 55-68页

作者： Yuming WU Xiaodong LUO Zhen YANG Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China China Telecom Corporation Limited Shanghai Branch Shanghai 200120 China Shanghai Research Institute of China Telecom Corporation Limited Shanghai 200120 China

Grammar learning has been a bottleneck problem for a long time. In this paper, we propose a method of seman- tic separator learning, a special case of grammar learning. The method is based on the hypothesis that some classes of words, called semantic separators, split a sentence into sev- eral constituents. The semantic separators are represented by words together with their part-of-speech tags and other infor- mation so that rich semantic information can be involved. In the method, we first identify the semantic separators with the help of noun phrase boundaries, called subseparators. Next, the argument classes of the separators are learned from cor- pus by generalizing argument instances in a hypernym space. Finally, in order to evaluate the learned semantic separators, we use them in unsupervised Chinese text parsing. The exper- iments on a manually labeled test set show that the proposed method outperforms previous methods of unsupervised text parsing.

关键词： semantic separator separator learning unsuper vised text parsing

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Time evolving graphical password for securing mobile devices 13

Time evolving graphical password for securing mobile devices

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8th ACM SIGSAC Symposium on Information, Computer and Communications Security, ASIA CCS 2013

作者： Wang, Zhan Jing, Jiwu Li, Liang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781450317672

Increasingly widespread use of mobile devices for processing monetary transactions and accessing business secrets has created a great demand on securing mobile devices. Poorly designed authentication mechanisms (e.g., screen lock and SIM card lock) on mobile devices either make users feel a hassle to lock the devices, or are vulnerable to attacks, such as shoulder surfing and smudge attack. In this paper, we propose a new login option for unlocking mobile devices called Time-Evolving Graphical Password (TEGP), which improves the strength of the password gradually over time by evolving the distortion degree of the images in the challenge portfolio without changing the pass images. By taking advantage of the extraordinary human ability to recall images, TEGP authenticates users by asking them to recognize the pass images which are transformed from the images uploaded by the user at registration. To achieve desired security and remain the usability, we present two metrics, Information Retention Rate (IRR) and Password Diversity Score (PDS), to advise the selection and distortion of the pass images and decoy images. Our experimental results show the memorability from the perspective of users, and the ability of TEGP to defend against various attacks. © 2013 ACM.

关键词： Edge detection

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Study on the Gain Material with Four Energy Level Model Using FDTD Method

Study on the Gain Material with Four Energy Level Model Usin...

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The International Symposium on Photonics and Optoelectronics(SOPO 2013)

作者： Hui Xue Zhixiang Huang Xianliang Wu Key Laboratory of Intelligent Computing and Signal Processing Anhui University Department of Physics and Electronic Engineering Hefei Normal University

A faster numerical method based on FDTD for the four energy level atomic system is present here. The initial conditions for the electrons of each level are achieving while the fields are in steady state. Polarization equation, rate equations of electronic population and Maxwell's equations were used to describe the coupling between the atoms and electromagnetic wave. Numerical simulations, based on a finite-difference time-domain (FDTD) method, were utilized to obtain the population inversion and lasing threshold. The validity of the model and its theory is confirmed. The time, which we can observe the lasing phenomenon, is much shorter in our new model. Our model can be put into using in large scale simulations in mutiphysics to reduce the total simulated time.

关键词： Finite-Difference Time-Domain Gain Material Lasing

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Refining image annotation by integrating PLSA with random walk model

Refining image annotation by integrating PLSA with random wa...

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19th International Conference on Advances in Multimedia Modeling, MMM 2013

作者： Tian, Dongping Zhao, Xiaofei Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9783642357244

In this paper, we present a new method for refining image annotation by integrating probabilistic latent semantic analysis (PLSA) with random walk (RW) model. First, we construct a PLSA model with asymmetric modalities to estimate the posterior probabilities of each annotating keywords for an image, and then a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity. Followed by a random walk process over the label graph is employed to further mine the correlation of the keywords so as to capture the refining annotation, which plays a crucial role in semantic based image retrieval. The novelty of our method mainly lies in two aspects: exploiting PLSA to accomplish the initial semantic annotation task and implementing random walk process over the constructed label similarity graph to refine the candidate annotations generated by the PLSA. Compared with several state-of-the-art approaches on Corel5k and Mirflickr25k datasets, the experimental results show that our approach performs more efficiently and accurately. © Springer-Verlag 2013.

关键词： Image annotation

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