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4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Sun, Jing Tian, Xuedong Liu, Dazhong Zhang, Zhiming School of Computer Science and Technology Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781138028302

With two-dimensional structures, mathematical expressions deliver more information than normal text not only with symbols but also their spatial arrangements. Users of mathematical retrieval systems need more search modes, such as fuzzy matching mode, rather than only exact matching mode to fit the features of mathematical expressions. Therefore, more and more researchers focus on how to retrieve mathematical expressions on the levels of structural similarity matching and synonyms matching. In this paper, an index structure based on Trie tree is designed oriented to measure the structural similarity of mathematical expressions. Firstly, the existing math indexing and matching method are introduced. Then, a stack algorithm of extracting information of math operators in LaTeX format is given. Finally, an index method based on Trie Tree is proposed for structural matching of mathematical expressions. The experimental analysis shows the effectiveness of the index method in this paper. © 2015 Taylor & Francis Group, London.

关键词： Search engines

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An Ordinal Random Forest and Its Parallel Implementation with MapReduce

An Ordinal Random Forest and Its Parallel Implementation wit...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shanshan Wang Junhai Zhai Sufang Zhang Hong Zhu School of Computer Science and Technology Hebei University Baoding China Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding China College of Mathematics Zhejiang Normal University Jinhua China Hebei Branch of Meteorological Cadres Training Institute China Meteorological Administration Baoding China

ISBN: (纸本)9781479986989

Ordinal decision tree (ODT) can effectively deal with monotonic classification problems. However, it is difficult for the existing ordinal decision tree algorithms to learning ODT from large data sets. Based on the variable consistency dominance based rough set approach (VC-DRSA), an ordinal random forest algorithm is proposed in this paper. Combining with the computing framework of MapReduce, the proposed ordinal random forest algorithm is paralleled on the platform of Hadoop, which improves the efficiency of the proposed algorithm. The feasibility and effectiveness of the proposed algorithm is verified by the experimental results.

关键词： Conferences Decision trees Vegetation

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Efficient image representation for object recognition via pivots selection

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Frontiers of computer science 2014年

作者： Xie, Bojun Liu, Yi Zhang, Hui Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well-known pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to "grow-up" features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive *** this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： Object recognition

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Audio retrieval based on Chinese keyword search in relational databases

Audio retrieval based on Chinese keyword search in relationa...

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3rd International Conference on Materials science and Information Technology, MSIT 2013

作者： Zhu, Bo Yan Liu, Guang Zhu, Liang College of Art Hebei University Baoding Hebei 071002 China Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9783037857700

In this paper, we propose a new method based on Chinese keyword search to select the WAV or MP3 files in audio post-production. First, we listen to each file and label it with Chinese characters, and then classify and store the files in a relational database system. Then, we use the techniques of Chinese keyword search to match query characters and the tuple characters quickly, and to compute similarities between the query and candidate tuples. For the characteristics of Chinese keyword search, we present a ranking strategy and an algorithm to refine the candidate tuples resulting from the first round matching, and finally get top-N results of audio files. The experimental results show that our method is efficient and effective. © (2013) Trans Tech Publications, Switzerland.

关键词： Relational database systems

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Core Set Extreme learning machine

Core Set Extreme Learning Machine

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作者： Shuxia Lu Bin Liu Caihong Jiao Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University School of Mechanical and Vehicular Engineering Beijing Institute of Technology

A core set extreme learning machine(CSELM) approach is proposed in order to deal with large datasets classification problem. In the first stage, the core set can be obtained efficiently by using the generalized core vector machine(GCVM) algorithm. For the second stage, the extreme learning machine(ELM) can be used to implement classification for much larger datasets. Experiments show that the CSELM has comparable performance with SVM and ELM implementations, but is faster on large datasets.

关键词： Core vector machine Extreme learning machine Support vector machine Core set

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An improved surround suppression model based on orientation contrast for boundary detection

An improved surround suppression model based on orientation ...

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International Conference on Pattern Recognition

作者： Hui Zhang Bojun Xie Jian Yu Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Hebei China Laboratory of Machine Learning and Cognitive Computation School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9781467322164

This paper proposes an unsupervised bottom-up boundary detection algorithm, which is an improved surround suppression model based on orientation contrast. First, the candidate boundary set is obtained by the edge focusing algorithm. Second, the orientation contrast map is constructed using the response of Gabor filter. The suppression term is computed on orientation contrast map using steerable filter, which can effectively differentiate step edge from texture edge. Using low-level image features, the boundary map can be used as preprocessing step for image segmentation and/or object detection. The detection approach has been validated on Rug dataset and the average of figure of merit shows an improvement of 15%.

关键词： Image edge detection Focusing Detection algorithms Detectors Image segmentation Educational institutions computer vision

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Evaluating Relational Ranking Queries Involving Both Text Attributes and Numeric Attributes

Evaluating Relational Ranking Queries Involving Both Text At...

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Conference on computer science and Software Engineering(CSSE 2012)

作者： Liang Zhu Zhaoliang Xie Qin Ma Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer ScienceHebei University Department of Foreign Language Teaching and Research Hebei University

In many database applications, ranking queries may reference both text and numeric attributes, where the ranking functions are based on both semantic distances/similarities for text attributes and numeric distances for numeric attributes. In this paper, we propose a new method for evaluating such type of ranking queries over a relational database. By statistics and training, this method builds a mechanism that combines the semantic and numeric distances, and the mechanism can be used to balance the effects of text attributes and numeric attributes on matching a given query and tuples in database search. The basic idea of the method is to create an index based on WordNet to expand the tuple words semantically for text attributes and on the information of numeric attributes. The candidate results for a query are retrieved by the index and a simple SQL selection statement, and then top-N answers are obtained. The results of extensive experiments indicate that the performance of this new strategy is efficient and effective.

关键词： Relational Database Ranking Query Semantic Distance Numeric Distance WordNet

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Chinese Keyword Search by Indexing in Relational Databases

Chinese Keyword Search by Indexing in Relational Databases

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Conference on computer science and Software Engineering(CSSE 2012)

作者： Liang Zhu Lijuan Pan Qin Ma Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer ScienceHebei University Department of Foreign Language Teaching and Research Hebei University

In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.

关键词： Relational Database Chinese Keyword Search Index Ranking Strategy

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Chinese Keyword Search by Indexing in Relational Databases

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Journal of Software Engineering and Applications 2012年第12期5卷 107-112页

作者： Liang Zhu Lijuan Pan Qin Ma Department of Foreign Language Teaching and Research Hebei University Baoding Hebei 071002 China Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China

关键词： Relational Database Chinese Keyword Search Index Ranking Strategy

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Evaluating Relational Ranking Queries Involving both Text Attributes and Numeric Attributes

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Journal of Software Engineering and Applications 2012年第12期5卷 88-93页

作者： Liang Zhu Zhaoliang Xie Qin Ma Department of Foreign Language Teaching and Research Hebei University Baoding Hebei 071002 China Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China

关键词： Relational Database Ranking Query Semantic Distance Numeric Distance WordNet

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