检索结果-内蒙古大学图书馆

2016 IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016

作者： Huang, Jiao Sun, Haobo Guo, Pengbo Zhao, Minghao Niu, Kai Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781509025930

In recent years, with the development of the Internet, it is more and more common for users to buy mobile phones on the Internet. On the one hand, sentiment analysis help customers to fully understand the performance of the phone, on the other hand, it provides improvement direction for producer to upgrade the goods. In this paper, we use the method of semantic analysis to analyze the sentiment of Chinese online comments about mobile phone. It is worth noting that we focus on the analysis of characteristics of different commentators for the product of the independent sentimental rather than for the product's overall feelings. On the basis of predecessors' work, we creatively put forward a solution to solve the problem of multiple-sentiment sentimental words, which remarkable improve the rate of recall. In the aspect of accuracy, we provide a more powerful method for the judgment of neutral words by analyzing the influence of the degree adverbs on neutral words. In the end of the article, several times with different experiments verify the effectiveness of the method in the aspect of accuracy rate and recall rate. © 2016 IEEE.

关键词： Cellular telephones

来源：评论

学校读者我要写书评

暂无评论

Sparse hybrid variational-Gibbs algorithm for latent Dirichlet allocation 16

Sparse hybrid variational-Gibbs algorithm for latent Dirichl...

引用

16th SIAM International Conference on Data Mining 2016, SDM 2016

作者： Li, Ximing Ouyang, Jihong Zhou, Xiaotang College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781510828117

Topic modeling algorithms such as the latent Dirichlet allocation (LDA) play an important role in machine learning research. Fitting LDA using Gibbs sampler-related algorithms involves a sampling process over K topics. We can use the sparsity in LDA to accelerate this expensive topic sampling process even for very large K values. However, LDA gradually loses sparsity as the number of documents increases. Motivated by the goal of fast LDA inference with large numbers of both topics and documents, in this paper we propose the novel sparse hybrid variational-Gibbs (SHVG) algorith-m. The SHVG algorithm divides the topic sampling probability into a sparse term that scales linearly with the number of per-document instantiated topics Kd, and a dense term that uses the Alias method to reduce the time cost to constant 0(1) time. This will lead to a significant improvement on efficiency. Using stochastic optimization techniques, we further develop an online version of SHVG for streaming documents. Experimental results on corpora with a wide range of sizes demonstrate the efficiency and effectiveness of the proposed SHVG algorithm. Copyright © by SIAM.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Detecting computer generated images based on local ternary count

引用

Pattern Recognition and Image Analysis 2016年第4期26卷 720-725页

作者： Tan, D.Q. Shen, X.J. Qin, J. Chen, H.P. College of Computer Science and Technology Jilin University Changchun Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin130012 China

Local binary patterns was used to distinguish the Photorealistic Computer Graphics and Photographic Images, however the dimension of the extracted features is too high. Accordingly, the Local Ternary Count based on the Local Ternary Patterns and the Local Binary Count was developed in this study. Furthermore, a novel algorithm is presented based on the Local Ternary Count to classify photorealistic Computer Graphics and Photographic images. The experiment results show that the proposed algorithm effectively reduces the dimension of the classification features and maintains a good classification performance. © 2016, Pleiades Publishing, Ltd.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Hyponymy Graph Model for Word Semantic Similarity Measurement

引用

Chinese Journal of Electronics 2015年第1期24卷 96-101页

作者： WANG Junhua ZUO Wanli PENG Tao College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering attached to the Ministry of Education

Measuring word semantic similarity is a generic problem with a broad range of applications such as ontology mapping, computational linguistics and artificial intelligence. Previous approaches to computing word semantic similarity did not consider concept occurrence frequency and word’s sense number. This paper introduced Hyponymy graph, and based on which proposed a novel word semantic similarity model. For two words to be compared, we first retrieve their related concepts; then produce lowest common ancestor matrix and distance matrix between concepts; finally calculate distance-based similarity and information-based similarity, which are integrated to get final semantic similarity. The main contribution of our method is that both concept occurrence frequency and word’s sense number are taken into account. This similarity measurement more closely fits with human rating and effectively simulates human thinking process. Our experimental results on benchmark dataset M&C and R&G with Word Net2.1 as platform demonstrate roughly 0.9%–1.2%improvements over existing best approaches.

关键词： Word semantic similarity Concept semantic similarity Semantic similarity Hyponymy graph model Word Net.

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems

引用

Scientific reports 2018年第1期8卷 838页

作者： Jiamei Liu Cheng Xu Weifeng Yang Yayun Shu Weiwei Zheng Fengfeng Zhou College of Software Jilin University Changchun Jilin 130012 China. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. College of Software Jilin University Changchun Jilin 130012 China. fengfengzhou@***. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. fengfengzhou@***.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems

引用

Scientific reports 2018年第1期8卷 4218页

作者： Jiamei Liu Cheng Xu Weifeng Yang Yayun Shu Weiwei Zheng Fengfeng Zhou College of Software Jilin University Changchun Jilin 130012 China. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. College of Software Jilin University Changchun Jilin 130012 China. FengfengZhou@***. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. FengfengZhou@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Checking linearizability with fine-grained traces 16

Checking linearizability with fine-grained traces

引用

31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Long, Zhenyue Zhang, Yu State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Laboratory of Symbol Computation and Knowledge Zhuhai College Jilin University China Information Center Guangdong Power Grid Co. Ltd. China

ISBN: (纸本)9781450337397

Linearizability is an important correctness criterion for concurrent objects and automatic checking of linearizability often involves searching a sequential witness in an exponentially growing space of traces. We present two acceleration techniques for checking linearizability based on fine-grained traces: one is based on speculation of linearization points, and the other is to separate read-only methods from the enumeration of serial witnesses. We have applied them to checking linearizability of eight different algorithms and the experiment shows that both can offer significant speed-up, allowing us to explore further on error analysis of concurrent objects based on fine-grained traces. © 2016 ACM.

关键词： fine-grained trace automatic verification linearizability

来源：评论

学校读者我要写书评

暂无评论

A FAST AND SCALABLE SUPERVISED TOPIC MODEL USING STOCHASTIC VARIATIONAL INFERENCE AND MAPREDUCE 5

A FAST AND SCALABLE SUPERVISED TOPIC MODEL USING STOCHASTIC ...

引用

2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Wenzhuo Song Bo Yang Xuehua Zhao Fei Li Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Digital Media Shenzhen Institute of Information Technology George Mason University

ISBN: (纸本)9781509012473

An important and widespread topic in cloud computing is text *** often use topic model which is a popular and effective technology to deal with related *** all the topic models,sLDA is acknowledged as a popular supervised topic model,which adds a response variable or category label with each document,so that the model can uncover the latent structure of a text dataset as well as retains the predictive power for supervised ***,sLDA needs to process all the documents at each iteration in the training *** the size of dataset increases to the volume that one node cannot deal with,sLDA will no longer be *** this paper we propose a novel model named *** which extends sLDA with stochastic variational inference(SVI) and *** can reduce the computational burden of sLDA and MapReduce extends the algorithm with *** makes the training become more efficient and the training method can be easily implemented in a large computer cluster or cloud *** results show that our approach has an efficient training process,and similar accuracy with sLDA.

关键词： Text classification MapReduce Variational inference Topic modeling

来源：评论

学校读者我要写书评

暂无评论

A modified evidence theory model for data fusion

A modified evidence theory model for data fusion

引用

IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Jie Liu Dayou Liu Xinhua Wang Ni Ni Yungang Zhu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University Changchun China State Key Laboratory of Applied Optics Chinese Academy of Sciences Changchun China Bejing Entry-Exit Inspection and Quarantine Bureau Beijing China

ISBN: (纸本)9781509045006

To overcome the drawbacks of traditional convex evidence, in this paper we proposed a modified convex evidence theory model, we presented the modified combination function and use it to combine mass function of ordered propositions, we present the calculation of the parameters of the proposed combination function, and proposed a more accurate method to find the proposition which is most likely true. The theoretical analysis and experimental results demonstrate that the proposed method has higher accuracy than traditional convex evidence.

关键词： inference mechanisms sensor fusion uncertainty handling Soft computing Inference mechanisms Sensor fusion Data fusion Evidence Theory Convexity mass function Proposition data integration

来源：评论

学校读者我要写书评

暂无评论

Statistical inference for community detection in signed networks

引用

Physical Review E 2017年第4期95卷 042313-042313页

作者： Xuehua Zhao Bo Yang Xueyan Liu Huiling Chen School of Digital Media Shenzhen Institute of Information Technology Shenzhen 518172 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China College of Physics and Electronic Information Wenzhou University Wenzhou 325035 China

The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to find community structure in such signed networks has become a research focus in the area of network science. Although many methods have been proposed to address the problem, their performance seriously depends on the predefined optimization objectives or heuristics which are usually difficult to accurately describe the intrinsic structure of community. In this study, we present a statistical inference method for community detection in signed networks, in which a probabilistic model is proposed to model signed networks and the expectation-maximization–based parameter estimation method is deduced to find communities in signed networks. In addition, to efficiently analyze signed networks without any a priori information, a model selection criterion is also proposed to automatically determine the number of communities. In our experiments, the proposed method is tested in the synthetic and real-word signed networks and compared with current methods. The experimental results show the proposed method can more efficiently and accurately find the communities in signed networks than current methods. Notably, the proposed method is a mathematically principled method.

关键词： Clustering Community structure Stochastic inference

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：