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international JOURNAL OF NEURAL SYSTEMS 2008年第1期18卷 19-31页

作者： Lebrun, Gilles Charrier, Christophe Lezoray, Olivier Cardot, Hubert Univ Caen Basse Normandie GREYC UMR 6072 CNRS Image Team F-14050 Caen France Univ Tours Lab Informat EA 2101 F-37200 Tours France

A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. the aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. the selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.

关键词： model selection metaheuristic tabu search machine learning Support Vector machines vector quantisation pattern recognition data mining

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Tabu search model selection for SVM

Tabu search model selection for SVM

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7th international conference on Intelligent data Engineering and Automated learning (IDEAL 2006)

关键词： model selection metaheuristic tabu search machine learning Support Vector machines vector quantisation pattern recognition data mining

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Unsupervised Sequential Forward Dimensionality Reduction Based On Fractal

Unsupervised Sequential Forward Dimensionality Reduction Bas...

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5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Yan, GuangHui Liu, LiSong Du, LinNa Yang, XiaXia Ma, ZhiCheng Lanzhou Jiaotong Univ Sch Informat & Elect Engn Lanzhou 730070 Peoples R China Gansu Elect Power Informat & Commun Cent Lanzhou 730050 Peoples R China

ISBN: (纸本)9780769533056

Dimensionality reduction has long been an active research topic within statistics, pattern recognition, machine learning and data mining. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. In this paper we transform the attribute selection problem into the optimization problem which tries to find the attribute subset with the maximal fractal dimension and the attribute number restriction simultaneously. In order to avoid exhaustive search in the huge attribute subset space we integrate the individual attribute priority with attribute subset evaluation for dimensionality reduction and propose the unsupervised Sequential Forward Fractal Dimensionality Reduction(SFFDR) algorithm. Our experiments on synthetic and real datasets show that the algorithm proposed can get the satisfied resulting attribute subset with a rather low time complexity.

关键词： data mining

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TWO-DIMENSIONAL LOCALITY SENSITIVE DISCRIMINANT ANALYSIS

TWO-DIMENSIONAL LOCALITY SENSITIVE DISCRIMINANT ANALYSIS

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6th international conference on Wavelet Analysis and pattern recognition

作者： Wei, Yantao Li, Hong Xia, Tian Huazhong Univ Sci & Technol Inst Pattern Recognit & Artifcial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Peoples R China Univ Adelaide Dept Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9781424422388

Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to rind the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional locality sensitive discriminant analysis (2DLSDA), which directly extracts the proper features from image matrices based on LSDA algorithm. And the experimental results on the ORL database show the effectiveness of the proposed algorithm. After that, 2DLSDA plus Fisherface, which was presented for the further dimensionality reduction, was compared with other dimention reduction methods, namely Eigenface, LSDA and 2DLSDA plus PCA. Experiments show that conducting Fisherface after 2DLSDA achieves high recognition accuracy.

关键词： Manifold learning Locality sensitive discriminant analysis Two-dimensional locality sensitive discriminant analysis Face recognition

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AGENERALIZED λ-FUZZY MEASURE AND ITS APPLICATION IN IMAGE ENHANCEMENT

AGENERALIZED λ-FUZZY MEASURE AND ITS APPLICATION IN IMAGE E...

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6th international conference on Wavelet Analysis and pattern recognition

作者： Yang, Lan-Zhen Ha, Ming-Hu Wang, Xian-Jie Zhang, Zi-Ru Hebei Univ Fac Math & Comp Sci Machine Learning Ctr Baoding 071002 Peoples R China Baoding Jiuhua Elect Engn Co Ltd 519 team North China Explorat Bureau Baoding 071002 Peoples R China Hebei Software Inst Baoding 071002 Peoples R China

ISBN: (纸本)9781424422388

In this paper, a new concept of generalized lambda-fuzzy measure is introduced, and it is a non-negative, extended real-valued set function satisfying generalized sigma-lambda-rule, and is not necessarily monotone. the relationships between this new concept and lambda-fuzzy measure, a signed measure and so on, are discussed, and it is worth noting that any generalized lambda-fuzzy measure is a signed measure under some conditions. Moreover, an application of generalized lambda-fuzzy measure in image enhancement is given, and the corresponding experiment shows that different values of lambda have different influences on an image.

关键词： Generalized sigma-lambda rule Generalized lambda-fuzzy measure Signed measure Null-additive Autocontinuous

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A machine learning methodology for the analysis of workplace accidents

A machine learning methodology for the analysis of workplace...

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6th international conference on Computational and Mathematical Methods in Science and Engineering

作者： Matias, J. M. Rivas, T. Martin, J. E. Taboada, J. Univ Vigo Dept Stat Vigo 36310 Spain Univ Vigo Dept Nat Resources Vigo 36310 Spain

this article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). the approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. the results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes.

关键词： machine learning statistics operations research data mining artificial intelligence

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A stagewise least square loss function for classification

A stagewise least square loss function for classification

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8th SIAM international conference on data mining 2008, Applied Mathematics 130

作者： Yang, Shuang-Hong Hu, Bao-Gang National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9781605603179

this paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex loss function in a stagewise manner. Several benefits are obtained from using the SLS loss function, such as: (i) higher generalization accuracy and better scalability than classical least square loss;(ii) improved performance and robustness than convex loss (e.g., hinge loss of SVM);(iii) computational advantages compared with nonconvex loss (e.g. ramp loss in ψ-learning);(iv) ability to resist myopia of Empirical Risk Minimization and to boost the margin without boosting the complexity of the classifier. In addition, it naturally results in a kernel machine which is as sparse as SVM, yet much faster and simpler to train. A fast online learning algorithm with an integrated sparsification procedure is also provided. Experimental results on several benchmarks confirm the advantages of the proposed approach.

关键词： Least squares approximations

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Enhanced services for targeted information retrieval by event extraction and data mining

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13th international conference on Applications of Natural Language to Information Systems

作者： Jungermann, Felix Morik, Katharina TU Dortmund Artificial Intelligence Unit Dortmund Germany

ISBN: (纸本)9783540698579

We present a framework combining information retrieval with machine learning and (pre-) processing for named entity recognition in order to extract events from a large document collection. the extracted events become input to a data mining component which delivers the final output to specific user's questions. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.

关键词： web-based information services knowledge extraction application framework

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Robust matching and recognition using context-dependent kernels 08

Robust matching and recognition using context-dependent kern...

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25th international conference on machine learning, ICML 2008

作者： Sahbi, Hichem Audibert, Jean-Yves Rabarisoa, Jaonary Keriven, Renaud CNRS - LTCI UMR 5141 Telecom ParisTech 46 rue Barrault 75013 Paris France Certis - Ecole des Ponts 6 avenue Blaise Pascal Cité Descartes 77455 Marne-la-Vallée France Willow - ENS INRIA 45 rue d'Ulm 75005 Paris France

ISBN: (纸本)9781605582054

the success of kernel methods including support vector machines (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics. We focus in this paper on object recognition using a new type of kernel referred to as "context-dependent". Objects, seen as constellations of local features (interest points, regions, etc.). are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching. (2) a neighborhood criterion which captures the object geometry and (3) a regularization term. We will show that the fixedpoint of this energy is a "context-dependent" kernel ("CDK") which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with "context-free" kernels.

关键词： Support vector machines

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A hybrid random subspace classifier fusion approach for protein mass spectra classification

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6th European conference on Evolutionary Computation, machine learning, and data mining in Bioinformatics, EvoBIO 2008

作者： Assareh, Amin Moradi, Mohammad Hassan Volkert, L. Gwenn Department of Biomedical Engineering Amirkabir University of Technology Tehran Iran Department of Computer Science Kent State University United States

ISBN: (纸本)3540787569

Classifier fusion strategies have shown great potential to enhance the performance of pattern recognition systems. there is an agreement among researchers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learning algorithm on multiple random replicates or sub-samples, in either feature space or the sample domain. In the present study we proposed a hybrid random subspace fusion scheme that simultaneously utilizes both the feature space and the sample domain to improve the diversity of the classifier ensemble. Experimental results using two protein mass spectra datasets of ovarian cancer demonstrate the usefulness of this approach for six learning algorithms (LDA, 1-NN, Decision Tree, Logistic Regression, Linear SVMs and MLP). the results also show that the proposed strategy outperforms three conventional re-sampling based ensemble algorithms on these datasets. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： learning algorithms

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