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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

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Discovering relations among entities from XML documents 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Wu, Yangyang Lei, Qing Luo, Wei Yokota, Harou Huaqiao Univ Dept Comp Sci Quanzhou Fujian Peoples R China Tokyo Inst Technol Dept Comp Sci Tokyo Japan

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

this paper addresses relation information extraction problem and proposes a method of discovering relations among entities which is buried in different nest structures of XML documents. the method first identifies and collects XML fragments that contain all types of entities given by users, then computes similarity between fragments based on semantics of their tags and their structures, and clusters fragments by similarity so that the fragments containing the same relation are clustered together, finally extracts relation instances and patterns of their occurrences from each cluster. the results of experiments show that the method can identify and extract relation information among given types of entities correctly from all kinds of XML documents with meaningful tags.

关键词： relation information extraction XML document cluster occurrence pattern

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A feature extraction method suitable for multi-channel sensor data

A feature extraction method suitable for multi-channel senso...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Sun, Li-Hui Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310012 Peoples R China

ISBN: (纸本)9781424410651

In order to obtain more significant information, the increasing hyper-dimensional data is acquired from multi-channel sensors, but the amount of data becomes very large. the quality of the data must be reduced for the data processing and transmission. the central problem is how, to extract the significant features from these data for these purposes. Optimal discrimination plane (ODP) technique based on Fisher's criterion method was developed to reduce the data in the paper. the patterns were projected onto the two orthogonal vectors that built up the ODP, and two-dimensional feature vectors were attained and utilized as features to represent the patterns. Electrocardiogram signals were applied to the analysis as an example in this study A quadratic discriminant function (QDF) based classifier and a threshold vector based classifier were employed to measure the performance of the extracted feature, respectively. the results show the proposed ODP is an effective and feasible technique to extract the features from the hyper-dimensional time series data.

关键词： multi-channel sensor data feature extraction classification

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A unified view of objective interestingness measures 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Hebert, Celine Cremilleux, Bruno Univ Caen GREYC CNRS UMR 6072 Campus Cote Nacre F-14032 Caen France

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Association rule mining often results in an overwhelming number of rules. In practice, it is difficult for the final user to select the most relevant rules. In order to tackle this problem, various interestingness measures were proposed. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we give a unified view of objective interestingness measures. We define a new framework embedding a large set of measures called SBMs and we prove that the SBMs have a similar behavior. Furthermore, we identify the whole collection of the rules simultaneously optimizing all the SBMs. We provide an algorithm to efficiently mine a reduced set of rules among the rules optimizing all the SBMs. Experiments on real datasets highlight the characteristics of such rules.

关键词： data mining

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A machine learning approach to test data generation: A case study in evaluation of gene finders 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Christiansen, Henning Dahmcke, Christina Mackeprang Roskilde Univ Dept Commun Business & Informat Technol Res Grp PLIS POB 260 DK-4000 Roskilde Denmark Roskilde Univ Dept Sci Syst & Models DK-4000 Roskilde Denmark

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. this has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). the quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrated these principles here, showing parts of a model under development for genetic sequences and indicate first initial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN, HMMGene and ***.

关键词： learning systems

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Mining maximal frequent itemsets in data streams based on FP-Tree 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Ao, Fujiang Yan, Yuejin Huang, Jian Huang, Kedi Natl Univ Def & Technol Sch Mech Engn & Automat Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Mining maximal frequent itemsets in data streams is more difficult than mining them in static databases for the huge, high-speed and continuous characteristics of data streams. In this paper, we propose a novel one-pass algorithm called FpMFI-DS, which mines all maximal frequent itemsets in Landmark windows or Sliding windows in data streams based on FP-Tree. A new structure of FP-Tree is designed for storing all transactions in Landmark windows or Sliding windows in data streams. To improve the efficiency of the algorithm, a new pruning technique, extension support equivalency pruning (ESEquivPS), is imported to it. the experiments show that our algorithm is efficient and scalable. It is suitable for mining MFIs both in static database and in data streams.

关键词： maximal frequent itemsets data streams FP-Tree pruning technique

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Segmentation of nuclei in confocal image stacks using performance based thresholding

Segmentation of nuclei in confocal image stacks using perfor...

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4th IEEE international Symposium on Biomedical Imaging

作者： Beaver, William Kosman, David Tedeschi, Gary Bier, Ethan McGinnis, William Freund, Yoav Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Cell & Dev Biol La Jolla CA 92093 USA

ISBN: (纸本)9781424406715

Combinatorial Transcriptional Fluorescent In Situ Hybridization (CT-FISH) is a confocal fluorescence imaging technique enabling the detection of multiple active transcription units in individual interphase diploid nuclei. As improved combinatorial labeling methods allow simultaneous measurement of gene activities to expand from five genes in a single embryo or tissue section to upward of twenty genes, transforming image stacks into usable data becomes an increasingly labor intensive task. In this paper we describe our progress towards a method for the computational analysis of confocal images from Drosophila melanogastar that involves the segmentation of the cell nuclei and of nascent transcription sites of specific genes. Using image processing and machine learning algorithms, we allow experimentalists to reiteratively tune and improve the analysis system to reflect biological reality.

关键词： image processing image segmentation machine vision pattern recognition

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Extension classification method and its application based on extensible set

Extension classification method and its application based on...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Yang, Chun-Yan Guangdong Univ Technol Res Inst Extens Engn Guangzhou 510090 Peoples R China

ISBN: (纸本)9781424410651

Based on the definitions of extensible set and the constructing method of its dependent function, a sort of classification method under extension tranformation, which is called extension classification method, is studied. It is different from the classification methods based on classical set, fuzzy set and rough set, and it is a sort of alterable classification method According to a certain transformation, it can divide a universe of discourse into 5 ports: positive extension field, negative extension field, positive stable field, negative stable field and extension boundary. Moreover, the universe of discourse and the dependent function describing the degree that an object possesses certain character are alterable. It makes the classification more elaborate. the phenomenon that "there is a corresponding classification pattern for a given transformation" is illuminated from the angle of set theory. Taking the extension classification management. on human resources as an example, its applied value will be explained. the classification method is a basic Method of extension data mining. It makes the classification function of data mining richer.

关键词： extensible set extension transformation classification extension classification data mining

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learning graph matching

Learning graph matching

引用

11th IEEE international conference on Computer Vision

作者： Caetano, Tiberio S. Cheng, Li Le, Quoc V. Smola, Alex J. NICTA Stat Machine Learning Program Canberra ACT 0200 Australia

ISBN: (纸本)9781424416301

As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. there are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. the main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NP-hard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the "labels" are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over state-of-the-art quadratic assignment relaxations. this finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed. this enables speed-ups of graph matching by up to 4 orders of magnitude while retaining state-of-the-art accuracy.

关键词： Computer vision

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Blog mining for the fortune 500 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Geller, James Parikh, Sapankumar Krishnan, Sriram New Jersey Inst Technol Dept Comp Sci Coll Comp Sci Newark NJ 07102 USA

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In recent years there has been a tremendous increase in the number of users maintaining online blogs on the Internet. Companies, in particular, have become aware of this medium of communication and have taken a keen interest in what is being said about them through such personal blogs. this has given rise to a new field of research directed towards mining useful information from a large amount of unformatted data present in online blogs and online forums. We discuss an implementation of such a blog mining application. the application is broadly divided into two parts, the indexing process and the search module. Blogs pertaining to different organizations are fetched from a particular blog domain on the Internet. After analyzing the textual content of these blogs they are assigned a sentiment rating. Specific data from such blogs along with their sentiment ratings are then indexed on the physical hard drive. the search module searches through these indexes at run time for the input organization name and produces a list of blogs conveying both positive and negative sentiments about the organization.

关键词： data mining

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Approximate inferring of goal-oriented Credal network

Approximate inferring of goal-oriented Credal network

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5th international conference on Wavelet Analysis and pattern recognition

作者： Li, Yu-Ling Wu, Qi-Zong Henan Univ Inst Data & Knowledge Engn Kaifeng 475001 Henan Peoples R China Beijing Inst Technol Sch Management & Econom Beijing 100081 Peoples R China

ISBN: (纸本)9781424410651

Approximate inferring approach of Credal network based on Ant Colony Algorithms is put forward Considering g network inferring of goal-oriented in Bayesian network for the variable decision-maker is interested in liven some evidence, the paper gives arithmetic for acquiring equivalent Credal network structure of goal-oriented Selecting of these vertexes in Credal network is considered as a multistage decision-making. Based on this, Ant Colony Algorithms is applied for Credal network approximate inferring to reuse the vertexes of high probability of each variable in order to improve of efficiency of inferring arithmetic and avoid some unnecessary computation. Finally, it shows the validity of the approach by simple analysis for a complex Credal network model.

关键词： Bayesian network Credal network goal-oriented Ant Colony Algorithms approximate inferring

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