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6th Annual Genetic and Evolutionary Computation conference, GECCO 2004

作者： Peterson, Michael R. Doom, Travis E. Raymer, Michael L. Department of Computer Science and Engineering Wright State University DaytonOH45345 United States

ISBN: (纸本)3540223444

the authors present a GA optimization technique for cosinebased k-nearest neighbors classification that improves predictive accuracy in a class-balanced manner while simultaneously enabling knowledge discovery. the GA performs feature selection and extraction by searching for feature weights and offsets maximizing cosine classifier performance. GA-selected feature weights determine the relevance of each feature to the classification task. this hybrid GA/classifier provides insight to a notoriously difficult problem in molecular biology, the correct treatment of water molecules mediating ligand binding to proteins. In distinguishing patterns of water conservation and displacement, this method achieves higher accuracy than previous techniques. the data mining capabilities of the hybrid system improve the understanding of the physical and chemical determinants governing favored protein-water binding. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data mining

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Neural network-based proper names extraction in fax images

Neural network-based proper names extraction in fax images

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international conference on pattern recognition

作者： N. Azzabou L. Likforman-Sulem Signal and Image Processing Laboratory & CNRS LTCI GET/Ecole Nationale Supérieure des Télécommunications Paris France

In this paper, we are interested in the sender's name extraction in fax cover pages through a machine learning scheme. For this purpose, two analysis methods are implemented to work in parallel. the first one is based on image document analysis (OCR recognition, physical block selection), the other on text analysis (word feature extraction, local grammar rules). Our main contribution consisted in introducing a neural network to find an optimal combination of the two approaches. Tests carried on real fax images show that the neural network improves performance compared to an empirical combination function and to each method used separately.

关键词： Neural networks Intelligent networks Image analysis data mining machine learning Text analysis Optical character recognition software Text recognition Feature extraction Electronic mail

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Selecting models from videos for appearance-based face recognition

Selecting models from videos for appearance-based face recog...

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international conference on pattern recognition

作者： A. Hadid M. Pietikainen Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering University of Oulu Finland

We propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. the approach is based on representing the face manifold in a low-dimensional space using the locally linear embedding (LLE) algorithm and then performing K-means clustering. We define the face models as the cluster centers. Our strategy is motivated by the efficiency of LLE to recover meaningful low-dimensional structures hidden in complex and high dimensional data such as face images. Two other well-known unsupervised learning algorithms (Isomap and SOM) are also considered. We compare and assess the efficiency of these different schemes on the CMU MoBo database which contains 96 face sequences of 24 subjects. the results clearly show significant performance enhancements over traditional methods such as the PCA-based one.

关键词： Face recognition Face detection Clustering algorithms Video sequences Principal component analysis Image databases Image recognition data mining Facial features machine vision

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Genetic feature learning algorithm for fluorescence fingerprinting of plants

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6th international conference Evolution Artificielle, EA 2003

作者： Codrea, Marius C. Aittokallio, Tero Keränen, Mika Tyystjärvi, Esa Nevalainen, Olli S. Turku Centre for Computer Science Lemminkäisenkatu 14 A TurkuFIN-20520 Finland Department of Information Technology University of Turku TurkuFIN-20014 Finland Department of Mathematics University of Turku TurkuFIN-20014 Finland Department of Biology Laboratory of Plant Physiology and Molecular Biology University of Turku TurkuFIN-20014 Finland

ISBN: (纸本)3540215239

Feature learning aims at automatic optimization of features to be used in the classification process. We consider the situation where, given a parameterized algorithm for extracting the features from the data, the optimizer tunes the parameters so that classification accuracy is maximized. the present paper extends our previous study [4] on feature learning problem by including two important mechanisms. First, an improved genetic algorithm (GA) with variable length chromosomes controls the size of the feature set. Second, the GA operates in conjuction with a neural network classifier for maximizing the identification accuracy. the performance of the feature learning algorithm is demonstrated with a problem of automatic identification of plant species from their fluorescence induction curves. the general approach should also be useful in other types of pattern recognition applications where a priori unknown characteristics are inferred from large feature spaces. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Genetic algorithms

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Competitive coding scheme for palmprint verification

Competitive coding scheme for palmprint verification

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international conference on pattern recognition

作者： A.W.-K. Kong D. Zhang Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada Biometrics Research Center Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

there is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. this paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. the competitive coding scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. this information is then stored in a feature vector called the competitive code. the angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. In our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3/spl times/10/sup -6/%. the execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.

关键词： Biometrics Feature extraction Gabor filters Security Automatic control Control systems data mining Iris Fingerprint recognition Humans

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Decision Fusion in Cooperative Adaptive Systems

Decision Fusion in Cooperative Adaptive Systems

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the 8th international conference on Computer Supported Cooperative Work in Design(第八届计算机支持的协同工作设计国际会议)(CSCWD2004)

作者： Mohamed Kamel Pattern Analysis and Machine Intelligence Lab.University of Waterloo Waterloo OntarioCanadaN2L 3G1

Research on cooperative, adaptive intelligentsystems, involves studying, developing and evaluatingarchitectures and methods to solve complex problemsusing adaptive and cooperative systems. these systemsmay range from simple software modules (such as aclustering or a classification algorithm) to physicalsystems (such as autonomous robots, machines orsensors).the main characteristic of these systems is that theyare adaptive and cooperative. By adaptive, it is meantthat the systems have a learning ability that makes themadjust their behaviour or performance to cope withchanging situations. the systems are willing tocooperate together to solve complex problems or toachieve common *** pattern recognition, there are notable contributionson the use of multiple classifiers. the most dominantdecomposition model used is an ensemble of classifiers(identical structures) that are trained differently. Mostof the innovations are in the combining methods. thereare weighting and voting approaches, probabilisticapproaches and approximate and fuzzy logicapproaches. In the area of sensor fusion, there havebeen some interesting ideas for fusing the data anddecisions of the sensors. However, most of thesecombining schemes are usually applied as a postprocessing *** this work are concerned with investigatingarchitectures and methods of aggregating decisions in amulti-classifier or multi-agent environment. Newarchitectures that allow active cooperation will bedeveloped. the classifiers (or agents) have to knowsome knowledge about others in the system. Differentforms of cooperation will be reported. In order for thesearchitectures to allow for dynamic decision fusion, theaggregation procedures have to have the flexibility toadapt to changes in the input and output and adjust toimprove on the final output. Changes will be learned bymeans of extracting features using feature *** of these architectures to problems inclassification of data, distributed data mining a

关键词： Adaptive systems Intelligent sensors Sensor fusion data mining Feature extraction Computer vision Intelligent systems Intelligent robots machine intelligence Computer architecture

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On the size of a classification tree

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3rd international conference on machine learning and data mining in pattern recognition

作者： Scaringella, A Univ Roma La Sapienza Fac Sociol I-00198 Rome Italy

We discuss some estimates for the misclassification rate of a classification tree in terms of the size of the learning set, following some ideas introduced in [3]. We develop some mathematical ideas of [3], extending ... 详细信息

ISBN: (纸本)3540405046

关键词： learning systems

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Fault diagnosis system for rotary machine based on fuzzy neural networks

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JSME international JOURNAL SERIES C-MECHANICAL SYSTEMS machine ELEMENTS AND MANUFACTURING 2003年第3期46卷 1035-1041页

作者： Zhang, S Asakura, T Xu, XL Xu, BJ Univ Fukui Fac Engn Fukui 9108507 Japan Univ Fukui Fac Engn Fukui 9108507 Japan Beijing Inst Machinery Ind Beijing 100085 Peoples R China

this paper is concerned with the application of fuzzy neural networks to fault diagnosis systems for rotary machines. In practical fault diagnosis, it is very difficult to improve the recognition rate of pattern recognition, especially when the sample data are similar. To solve these difficulties, a fault diagnosis system using fuzzy neural networks is proposed in this research. A fault diagnosis system with fuzzy neural networks is based on a series of standard fault pattern pairings between fault symptoms and fault. Fuzzy neural networks are trained to memorize these standard pattern pairs. Unlike other neural networks, fuzzy neural networks adopt bi-directional association. they make use of information from both the fault symptoms and the fault patterns, which can improve recognition rate greatly. When an unknown sample becomes the input for a trained fault diagnosis system, the fault diagnosis system can make fault diagnosis by bi-directional association of fuzzy neural networks. through experiments with a rotor testing table and applications in monitoring and fault diagnosis of water pump sets of oil plant, it is verified that fuzzy neural networks have a well distinguished ability and are effective to perform fault diagnosis of rotary machines.

关键词： fuzzy neural networks fault diagnostics rotary machine vibration pattern recognition bi-directional association

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A machine learning model for information retrieval with structured documents

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3rd international conference on machine learning and data mining in pattern recognition

作者： Piwowarski, B Gallinari, P Univ Paris 06 LIP6 F-75015 Paris France

ISBN: (纸本)3540405046

Most recent document standards rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. Only a few models have been proposed for handling structured documents, and the design of such systems is still an open problem. We present here a new model for structured document retrieval which allows to compute and to combine the scores of document parts. It is based on bayesian networks and allows for learning the model parameters in the presence of incomplete data. We present an application of this model for ad-hoc retrieval and evaluate its performances on a small structured collection. the model can also be extended to cope with other tasks such as interactive navigation in structured documents or corpus.

关键词： Information retrieval systems

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learning pedestrian models for silhouette refinement

Learning pedestrian models for silhouette refinement

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9th IEEE international conference on Computer Vision

作者： Lee, L Dalley, G Tieu, K MIT Cambridge MA 02139 USA

ISBN: (纸本)0769519504

We present a model-based method for accurate extraction of pedestrian silhouettes from video sequences. Our approach is based on two assumptions, 1) there is a common appearance to all pedestrians, and 2) each individual looks like him/herself over a short amount of time. these assumptions allow us to learn pedestrian models that encompass both a pedestrian population appearance and the individual appearance variations. Using our models, we are able to produce pedestrian silhouettes that have fewer noise pixels and missing parts. We apply our silhouette extraction approach to the NIST gait data set and show that under the gait recognition task, our model-based sulhouettes result in much higher recognition rates than silhouettes directly extracted from background subtraction, or any non-model-based smoothing schemes.

关键词： data mining

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