检索结果-内蒙古大学图书馆

9th International Conference on Semantics, Knowledge and Grids, SKG 2013

作者： Zhang, Jianhua Wang, Wenjie Shi, Zhongzhi Tian, Dongping Yue, Jinpeng Zhang, Bo Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479930128

Web service composition is one of the fundamental technologies in implementing Service Oriented Architecture (SOA) based applications. Description Logic based Web service composition can easily express the static knowledge, however it is lack the dynamic knowledge support. Dynamic Description Logic extends from Description Logic and it can represent both static and dynamic knowledge. In this paper, we propose a Web service composition method base on AI planning which introduce the action dimension to the Web service composition. Finally, we give a simple example to show the algorithm's validity. © 2013 IEEE.

关键词： Computer circuits

来源：评论

学校读者我要写书评

暂无评论

Semantic separator learning and its applications in unsupervised Chinese text parsing

引用

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

来源：评论

学校读者我要写书评

暂无评论

A directed continuous time random walk model with jump length depending on waiting time

The Scientific World Journal

引用

The Scientific World Journal 2014年第1期2014卷 182508页

作者： Shi, Long Yu, Zuguo Mao, Zhi Xiao, Aiguo Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China Institute of Mathematics and Physics Central South University of Forest and Technology Changsha Hunan 410004 China School of Mathematical Sciences Queensland University of Technology Brisbane QLD 4001 G.P.O. Box 2434 Australia

In continuum one-dimensional space, a coupled directed continuous time random walk model is proposed, where the random walker jumps toward one direction and the waiting time between jumps affects the subsequent jump. In the proposed model, the Laplace-Laplace transform of the probability density function P (x, t) of finding the walker at position x at time t is completely determined by the Laplace transform of the probability density function (t) of the waiting time. In terms of the probability density function of the waiting time in the Laplace domain, the limit distribution of the random process and the corresponding evolving equations are derived. © 2014 Long Shi et al.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Shared structure learning for multiple tasks with multiple views

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A motivational system for mind model CAM

A motivational system for mind model CAM

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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)

来源：评论

学校读者我要写书评

暂无评论

Phrase filtering for content words in hierarchical phrase-based model

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Color image segmentation based on dynamic model and power spectrum

Color image segmentation based on dynamic model and power sp...

引用

International Conference on Materials Science and Manufacturing, ICMSM 2012

作者： Chen, Yupu Qiao, Yuanhua Miao, Jun College of Applied Sciences Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783037855614

In this paper, the locally coupled neural oscillator model and power spectrum are introduced to solve the color image segmentation problem. Firstly the single neuron oscillator model is developed, the model parameter is set so as to create limit circle simulating the activity of neuron. Then the locally coupled network model is developed, taking R, G, Bas external input separately, the network output three dynamic orbits representing R, G, B for each image point. Lastly, the power spectrum of the orbits is calculated for each image point, the color image is segmented using the calculated power spectrum. The performance is comparable to the results from other segmentation methods. © (2013) Trans Tech Publications, Switzerland.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Refining image annotation by integrating PLSA with random walk model

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

引用

15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Shang, Tianfeng He, Qing Zhuang, Fuzhen Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate School Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9783642374005

Collaborative filtering is one of the most popular techniques in recommender systems, and the key point is to find similar users and items. There are already some similarity measures, such as vector cosine similarity and Pearson's correlation coefficient, and so on. However, in some cases, what recommender systems get are not the ratings, but preference sequences of users on a series of items. For this type of data, those traditional similarity measures may fail to meet the practical application requirements. In this paper, a similarity measure based on inversion is proposed for preference sequences naturally. Based on the Inversion similarity measure, some structural information of user preference sequences is analyzed. By merging average precision and weighted inversion into similarity computation, a new similarity measure based on preference sequences is proposed for collaborative filtering. Experimental results show that the proposed similarity measure based on preference sequences outperforms the common similarity measures on the datasets with continuous real numbers. © 2013 Springer-Verlag.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：