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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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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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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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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

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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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Feature extended short text categorization based on theme ontology

Feature extended short text categorization based on theme on...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yan Zhan Hao Chen Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China College of Mathematics and Computer Science Hebei University Baoding China

Short text classification problem as text classification a branch, in addition to the same with traditional text classification to a certain degree, still need to face some special problems to be solved, because of short text length, features sparse, measuring the words difficultly. Due to the nature of ontology emphasize related field concept, which has the defect of few characteristics in short text, it is necessary to emphasize the relationship between semantic. This paper uses the feature extended method based on theme Ontology. As considering the semantic relations, it can get better classification performance compared to the conventional method. Meanwhile, using Case-Base Maintenance learning via the GC (Generalization Capability) algorithm, which can reduce the case number into K-NN algorithm, can improve efficiency when indexing near neighbor in K-Nearest Neighbor algorithm. The numerical experiments prove the validity of this learning algorithm.

关键词： Ontologies Text categorization Semantics Classification algorithms Algorithm design and analysis Educational institutions Training

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Chinese Functional Chunk Identification Based on Part of Speech

Chinese Functional Chunk Identification Based on Part of Spe...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Cheng-Yan Wang Xin-Fu Li Xue-Dong Tian College of Mathematics and Computer Science Hebei University Baoding China College of Mathematics and Computer Scienc Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province Hebei University Baoding China

Chinese functional chunk describes the basic skeleton of the Chinese sentences. It is the important bridge for joining syntax and semantic description, and the Chinese functional chunk identification plays a key role in the field of natural language processing. Identifying the functional chunk is mostly based on methods of rules which come from the syntactic analysis. In this paper, the functional chunk is mainly identified according to the effect of the part of speech in the syntactic analysis. The experimental results show the proposed method works well on the functional chunk identification.

关键词： Tagging Speech Syntactics Educational institutions Statistical analysis Information processing Particle separators

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An image retrieval method about ancient Chinese characters within local area

An image retrieval method about ancient Chinese characters w...

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Tian, Xue-Dong Huang, Juan Yang, Fang Ha, Yan College of Mathematics and Computer Science Hebei University Baoding Hebei China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

ISBN: (纸本)9781457717017

Ancient Chinese characters normally have complex structures which are composed of many strokes. Different characters may show a similar shape which results in unsatisfactory answers for their image retrieving using the traditional methods. In this paper, an image retrieval algorithm for ancient Chinese characters is proposed based on the local area of characters. First of all, according to the need of users, the regions needed to be matched are marked on the sample of ancient Chinese character image. Then we match the image by comparing the Euclidean distance of feature parameters between sample image and database images. Finally, the ancient images which users need are found. The experimental results show that the proposed method could improve the efficiency of correlative research. © 2011 IEEE.

关键词： Image retrieval

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KM-A∗ pathfinding algorithm based on hierarchical clustering and strengthened DB Index criteria

KM-A∗ pathfinding algorithm based on hierarchical clusterin...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Cai Chen Yan Li Tie-Song Li Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

In computer games, high-quality pathfinding algorithms are important to bring satisfactory experiences to the players, which may improve the playability of computer game. The method of KM-A* belongs to hierarchical pathfinding, which incorporates the information of obstacle distribution when partitioning game maps using K-means clustering. This algorithm reduced unnecessary storage and unnatural paths in some open areas. However, the found path by KM-A* is very unstable and the quality closely depends on the clustering results. In this paper, we proposed an improved KM-A* algorithm, which aims to enhance the stability and quality of pathfinding. First, an agglomerative hierarchical clustering algorithm is used to determine the initial cluster centers, and K-Means algorithm is then applied to cluster obstacle nodes with k values which can possibly generate best cluster categories on game maps. Secondly, the strengthened form of DB Index criteria is defined by adding impact factors to the standard DB Index criteria to measure the quality of clustering results. Finally the best clustering is selected based on this new DB Index, which will provide a guidance to further generate an abstract map for on-line pathfinding. Experiments are conducted to compare the performance of KM-A* and improved KM-A*, which show that the latter can find more stable paths and use less on-line processing time.

关键词： Indexes Clustering algorithms Games Partitioning algorithms machine learning Algorithm design and analysis Heuristic algorithms

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