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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2835 条记录，以下是2411-2420 订阅

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Normalized linear transform for cross-platform microarray data integration

Normalized linear transform for cross-platform microarray da...

引用

6th international conference on machine learning and Applications

作者： Xiong, Huilin Zhang, Ya Chen, Xue-wen Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Univ Kansas Univ Elect Engn & Comp Sci Lawrence KS 66045 USA

ISBN: (纸本)9780769530697

With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. the NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.

关键词： data integration

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An effective method for classification of high dimensional data

An effective method for classification of high dimensional d...

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6th international conference on machine learning and Cybernetics

作者： Lam, Benson S. Y. Yan, Hong City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781424409723

We study a new high dimensional data problem in this paper. In pattern classification, if many dimensions of two groups share a similar distribution, the classification error rates will be 50%. We have proposed a new clustering algorithm to deal with this problem. Its basic idea is to confine the support of the optimization equation so that the data points in one group can only have small contribution to the estimated cluster center in another group. Experiments show that the proposed method is able to yield good results in eight real world data sets and its performance is better than 10 existing methods.

关键词： machine learning classification of high imensional data clustering calculus of variations

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Distance metric learning through optimization of ranking

Distance metric learning through optimization of ranking

引用

17th IEEE international conference on data mining Workshops, ICDM Workshops 2007

作者： Gopal, Kreshna Ioerger, thomas R. Department of Computer Science Texas A and M University

ISBN: (纸本)0769530192

data preprocessing is important in machine learning, data mining, and pattern recognition. In particular, selecting relevant features in high-dimensional data is often necessary to efficiently construct models that accurately describe the data. For example, many lazy learning algorithms (like k-Nearest Neighbor) rely on feature-based distance metrics to compare input patterns for the purpose of classification or retrieval from a database. In previous work, we introduced Slider, a distance metric learning method that optimizes the weights of features in a protein model-building application (where features are used to describe patterns of electron density around protein macromolecules). In this work, we demonstrate the usefulness of Slider as a general method for classification, ranking and retrieval, with results on several benchmark datasets. We also compare it to other well-known feature selection or weighting methods. © 2007 IEEE.

关键词： Classification (of information)

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Support vector machines for ranking learning: the full and the truncated fixed margin strategies

Support vector machines for ranking learning: The full and t...

引用

6th international conference on machine learning and Cybernetics

作者： Tatarchuk, Alexander Kurakin, Alexey Mottl, Vadim Russian Acad Sci Ctr Comp Vavilov St 40 Moscow 119991 Russia Moscow Phys Tech Inst Dolgoprudnyi 141700 Russia

ISBN: (纸本)9781424409723

Two known SVM-based approaches to ranking learning (ordinal regression estimation, supervised pattern recognition with ordered classes) are scrutinized as different generalizations of the classical principle of finding the optimal discriminant hyperplane in a linear space. Easily verifiable natural conditions are found under which the training result obtained by the computationally much more attractive truncated technique is completely equivalent to the hypothetical strict solution. the numerical procedures are essentially simplified for both techniques.

关键词： ordinal regression ranking learning large margin learning support vector machine computational complexity

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Combination of cloud model and neural network to find knowledge from imperfect data in IDSS

Combination of cloud model and neural network to find knowle...

引用

6th international conference on machine learning and Cybernetics

作者： Wang, Hong-Li Zhongyuan Univ Technol Sch Econ & Management Zhengzhou 450007 Peoples R China

ISBN: (纸本)9781424409723

Decision support system using data mining to find decision knowledge is called Intelligent Decision Support System (IDSS). Neural network as a data mining method commonly is used to find classification knowledge in IDSS. But the classic data mining based on neural network is short in dealing with the blank value data or data with the character of blurring and randomicity. Such data is called as imperfect data. In order to overcoming this shortcoming the method of combination of cloud model and neural network to find knowledge from imperfect data in IDSS is proposed. Firstly the cloud is used to depict the imperfect data by group decision. In the following, attribution generation based on cloud model or grey cloud model is used to generate the upper concept layer. In this step the cloud model depicting the imperfect data is classified into the concept layer that is proximal to itself according to distance between two cloud models. then classic neural network method is used to gain knowledge. the data is input into the neural network to training and gaining the classification knowledge. Lastly an experiment is given to verify the validity of the method.

关键词： decision support system data mining cloud model neural network imperfectness of data data pretreatment

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Kernel methods for mining instance data in ontologies

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6th international Semantic Web conference/2nd Asian Semantic Web conference (ISWC 2007/ASWC 2007)

作者： Bloehdorn, Stephan Sure, York Univ Karlsruhe Inst AIFB Kaiserstr 12 D-76128 Karlsruhe Germany SAP AG Res Ctr CEC Karlsruhe D-76131 Karlsruhe Germany

ISBN: (纸本)9783540762973

the amount of ontologies and meta data available on the Web is constantly growing. the successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

关键词： Ontology

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FACE recognition RESEARCH BASED ON ANTI-SYMMETRICAL WAVELET AND EIGENFACE

FACE RECOGNITION RESEARCH BASED ON ANTI-SYMMETRICAL WAVELET ...

引用

6th international conference on machine learning and Cybernetics

作者： Yin, Qian Yuan, Zhi-Yong Kong, Ying Guo, Ping Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781424409723

In this paper, a new human face recognition method based on anti-symmetrical biorthogonal wavelet transformation (ASBWT) and eigenface was proposed. First the anti-symmetrical biorthogonal wavelet is chosen to degrade the face image dimension, meanwhile complete the process of face location and segmentation;And then human face is reverted through the face space of Eigenface, the traditional average human face is replaced in the within-class scatter matrix. this within-class scatter matrix is used to calculate within-class and between-class distance proportion as a rule function, calculate the twice eigenface through Discrete Karhunen-Loeve Transform (DKLT), and use Singular Value Decomposition (SVD) method to calculate the eigenvector. Finally we compute the weights and classify the face images. the results show that the proposed method has higher recognition rate and more robust than the traditional eigenface analysis method.

关键词： face recognition anti-symmetrical biorthogonal wavelet eigenface SVD

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An incremental fuzzy decision tree classification method for mining data streams 1

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

作者： Wang, Tao Li, Zhoujun Yan, Yuejin Chen, Huowang Natl Univ Def Technol Comp Sch Changsha 410073 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100083 Peoples R China

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

One of most important algorithms for mining data streams is VFDT. It uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. Gama et al. have extended VFDT in two directions. their system VFDTc can deal with continuous data and use more powerful classification techniques at tree leaves. In this paper, we revisit this problem and implemented a system fVFDT on top of VFDT and VFDTc. We make the following four contributions: 1) we present a threaded binary search trees (TBST) approach for efficiently handling continuous attributes. It builds a threaded binary search tree, and its processing time for values inserting is O(nlogn), while VFDT's processing time is O(n(2)). When a new example arrives, VFDTc need update O(logn) attribute tree nodes, but fVFDT just need update one necessary node.2) we improve the method of getting the best split-test point of a given continuous attribute. Comparing to the method used in VFDTc, it improves from O(nlogn) to O (n) in processing time. 3) Comparing to VFDTc, fVFDT's candidate split-test number decrease from O(n) to O(logn).4)lmprove the soft discretization method to be used in data streams mining, it overcomes the problem of noise data and improve the classification accuracy.

关键词： data streams incremental fuzzy continuous attribute threaded binary search tree

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A graph classification approach using a multi-objective genetic algorithm application to symbol recognition

A graph classification approach using a multi-objective gene...

引用

6th international Workshop on Graph-Based Representations in pattern recognition

作者： Raveaux, Romain Eugen, Barbu Locteau, Herve Adam, Sebastien Heroux, Pierre Trupin, Eric Univ La Rochelle L3I Lab La Rochelle France Univ Rouen LITIS Labs F-76821 Mont St Aignan France

ISBN: (纸本)9783540729020

In this paper, a graph classification approach based on a multi-objective genetic algorithm is presented. the method consists in the learning of sets composed of synthetic graph prototypes which are used for a classification step. these learning graphs are generated by simultaneously maximizing the recognition rate while minimizing the confusion rate. Using such an approach the algorithm provides a range of solutions, the couples (confusion, recognition) which suit to the needs of the system. Experiments are performed on real data sets, representing 10 symbols. these tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.

关键词： graph classification multi-objective optimization machine learning graph dissimilarity measure

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mining association rules in scale-free networks

Mining association rules in scale-free networks

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6th international conference on machine learning and Cybernetics

作者： Gao, Li Dai, Shang-Ping Zhu, Chang-Wu Hua Zhong Normal Univ Dept Comp Sci Wuhan 430079 Peoples R China

ISBN: (纸本)9781424409723

For the characteristic of scale-free networks, containing a few nodes that have a very high degree and many with low degree,the high connectivity nodes play an important role of hubs in communication and networking. this characteristic can be exploited with designing efficient search algorithms. this paper proposes an algorithm to change each new node connecting to the network based on its high-degree-probability for equal-degree-probability after having constituted initial model by choosing high-degree-probability nodes. We use an association rule search strategy that utilizes high degree nodes in scale-free networks and costs scaling with the size of the graph. We also demonstrate the utility of these CSCCNU network.. It can improve networks' robustness.

关键词： data mining genetic algorithm association rules hubs scale-free networks

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