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Joint IAPR international Workshops on 10th Structural, Syntactic, and Statistical pattern recognition, SSPR 2004 and 5th international Workshop on Statistical Techniques in pattern recognition, SPR 2004

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Joint IAPR 10th international Workshop on Structural and Syntactic pattern recognition, SSPR 2004 and 5th international Workshop on Statistical Techniques in pattern recognition, SPR 2004

ISBN: (纸本)9783540225706

the proceedings contain 127 papers. the special focus in this conference is on Visual recognition, Detection, Contours, Lines and Paths. the topics include: Finding clusters and components by unsupervised learning;a demonstration on text data;the use of graph techniques for identifying objects and scenes in indoor building environments for mobile robots;graphical-based learning environments for pattern recognition;spectral analysis of complex laplacian matrices;a significant improvement of softassign with diffusion kernels;eigenspace method by autoassociative networks for object recognition;extraction of skeletal shape features using a visual attention operator;computing the cyclic edit distance for pattern classification by ranking edit paths;steady state random walks for path estimation;new variational framework for rigid-body alignment;an error-tolerant approximate matching algorithm for attributed planar graphs and its application to fingerprint classification;comparison of algorithms for web document clustering using graph representations of data;a syntactic pattern recognition approach to computer assisted translation;a general methodology for finite-state translation using alignments;a comparison of unsupervised shot classification algorithms for news video segmentation;diagnosis of lung nodule using the semivariogram function;distances between distributions;multiscale curvature assessment of postural deviations;learning people movement model from multiple cameras for behaviour recognition;a comparison of least squares and spectral methods for attributed graph matching and an auction algorithm for graph-based contextual correspondence matching.

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Locally tuned general regression for learning mixture models using small incomplete data sets with outliers and overlapping classes

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5th international conference on Intelligent data Engineering and Automated learning (IDEAL 2004)

作者： Rafat, A Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

ISBN: (纸本)3540228810

Finite mixture models are commonly used in pattern recognition. Parameters of these models are usually estimated via the Expectation Maximization algorithm. this algorithm is modified earlier to handle incomplete data. However, the modified algorithm is sensitive to the occurrence of outliers in the data and to the overlap among data classes in the data space. Meanwhile, it requires the number of missing values to be small in order to produce good estimations of the model parameters. therefore, a new algorithm is proposed in this paper to overcome these problems. A comparison study shows the preference of the proposed algorithm to other algorithms commonly used in the literature including the modified Expectation Maximization algorithm.

关键词： pattern recognition

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Classification of lung data by sampling and support vector machine

Classification of lung data by sampling and support vector m...

引用

conference Proceedings - 26th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004

作者： Dehmeshki, Jamshid Chen, Jun Manlio Casique, Valdivieso Karakoy, Mustafa MedicSight PLC 46 Berkeley Square Mayfair London W1J 5AT United Kingdom

Developing a Computer-Assisted Detection (CAD) system for automatic detection of pulmonary nodules in thoracic CT is a highly challenging research area in the medical domain. It requires the application of state-of-the-art image processing and pattern recognition technologies. the object recognition and feature extraction phase of such a system generates a large number of data set. As there is normally a large quantity of non-nodule objects within this data set while the nodule objects are sparse, a Gaussian mixture model-based sampling method is used to reduce the non-nodule data and thus the classification complexity. the support vector machine, a classifier motivated from the statistical learning theory, is used in the pattern recognition stage of automatic pulmonary nodule detection. After the training process, only support vectors will be used in the classification process. As the support vector machine classifier gives the unique optimal solution, the experiment on the lung nodule data shows a fast and satisfactory classification rate.

关键词： Computerized tomography

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5th international conference on Intelligent data Engineering and Automated learning, IDEAL 2004

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5th international conference on Intelligent data Engineering and Automated learning, IDEAL 2004

ISBN: (纸本)3540228810

the proceedings contain 125 papers. the special focus in this conference is on Bioinformatics, data mining and Knowledge Engineering. the topics include: Modelling and clustering of gene expressions using RBFs and a shape similarity metric;a novel hybrid GA/SVM system for protein sequences classification;building genetic networks for gene expression patterns;SVM-based classification of distant proteins using hierarchical motifs;knowledge discovery in lymphoma cancer from gene-expression;a method of filtering protein surface motifs based on similarity among local surfaces;qualified predictions for proteomics pattern diagnostics with confidence machines;an assessment of feature relevance in predicting protein function from sequence;a new artificial immune system algorithm for clustering;the categorisation of similar non-rigid biological objects by clustering local appearance patches;unsupervised dense regions discovery in DNA microarray data;visualisation of distributions and clusters using ViSOMs on gene expression data;prediction of implicit protein-protein interaction by optimal associative feature mining;exploring dependencies between yeast stress genes and their regulators;prediction of natively disordered regions in proteins using a bio-basis function neural network;the effect of image compression on classification and storage requirements in a high-throughput crystallization system;a hybrid approach to human core-promoter prediction;synergy of logistic regression and support vector machine in multiple-class classification;deterministic propagation of blood pressure waveform from human wrists to fingertips;pre-pruning decision trees by local association rules and a new approach for selecting attributes based on rough set theory.

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mining large engineering data sets on the grid using AURA

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5th international conference on Intelligent data Engineering and Automated learning (IDEAL 2004)

作者： Liang, B Austin, J Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)3540228810

AURA (Advanced Uncertain Reasoning Architecture) is a parallel pattern matching technology intended for high-speed approximate search and match operations on large unstructured datasets. this paper represents how the AURA technology is extended and used to search the engine data within a major UK eScience Grid project (DAME) for maintenance of Rolls-Royce aero-engines and how it may be applied in other areas. Examples of its use will be presented.

关键词： pattern matching

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Strength in diversity: the advance of data analysis 8th

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15th European conference on machine learning, ECML 2004

作者： Hand, David J. Department of Mathematics Imperial College 180 Queen’s Gate LondonSW7 2AZ United Kingdom

ISBN: (纸本)9783540231059

the scientific analysis of data is only around a century old. For most of that century, data analysis was the realm of only one discipline - statistics. As a consequence of the development of the computer, things have changed dramatically and now there are several such disciplines, including machine learning, pattern recognition, and data mining. this paper looks at some of the similarities and some of the differences between these disciplines, noting where they intersect and, perhaps of more interest, where they do not. Particular issues examined include the nature of the data with which they are concerned, the role of mathematics, differences in the objectives, how the different areas of application have led to different aims, and how the different disciplines have led sometimes to the same analytic tools being developed, but also sometimes to different tools being developed. Some conjectures about likely future developments are given. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data mining

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Mobile data mining by location dependencies

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5th international conference on Intelligent data Engineering and Automated learning, IDEAL 2004

作者： Goh, Jen Ye Taniar, David Monash University School of Business Systems ClaytonVIC3800 Australia

ISBN: (纸本)3540228810

Mobile mining is about finding useful knowledge from the raw data produced by mobile users. the mobile environment consists of a set of static device and mobile device. Previous works in mobile data mining include finding frequency pattern and group pattern. Location dependency was not part of consideration in previous work but it would be meaningful. the proposed method builds a user profile based on past mobile visiting data, filters and to mine association rules. the more frequent the user profiles are updated, the more accurate the rules are. Our performance evaluation shows that as the number of characteristics increases, the number of rules will increase dramatically and therefore, a careful choosing of only the relevant characteristics to ensure acceptable amount of rules. © Springer-Verlag Berlin Heidelberg 2004.

关键词： User profile

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the rough set database system: An overview 4th

引用

4th international conference on Rough Sets and Current Trends in Computing

作者： Suraj, Z Grochowalski, P Univ Informat Technol & Management Chair Comp Sci Fdn Rzeszow Poland Rzeszow Univ Math Inst Rzeszow Poland

ISBN: (纸本)3540221174

the paper describes the "Rough Sets database System" (called in short the RSDS system) for the creation of bibliography on rough sets and their applications. this database is the most comprehensive online rough sets bibliography and accessible under the following web-site address: http://*** the service has been developed in order to facilitate the creation of rough sets bibliography, for various types of publications. At the moment the bibliography contains over 1400 entries from more than 450 authors. It is possible to create the bibliography in HTML or BibTeX format. In order to broaden the service contents it is possible to append new data using specially dedicated form. After appending data online the database is updated automatically. If one prefers sending a data file to the database administrator, please be aware that the database is updated once a month. In the current version of the RSDS system, there is the possibility for appending to each publication an abstract and keywords. As a natural consequence of this improvement there exists a possibility for searching a publication by keywords.

关键词： rough sets fuzzy systems neural networks evolutionary computing data mining knowledge discovery pattern recognition machine learning database systems

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LMIK - learning Medical Image Knowledge: An Internet-based medical image knowledge acquisition framework

LMIK - Learning Medical Image Knowledge: An Internet-based m...

引用

5th conference on Internet Imaging

作者： Rudrapatna, M Sowmya, A Zrimec, T Wilson, P Kossoff, G Lucas, P Wong, J Misra, A Busayarat, S Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

ISBN: (纸本)0819452076

As part of the learning Medical Imaging Knowledge project, we are developing a knowledge-based, machine learning and knowledge acquisition framework for systematic feature extraction and recognition of a range of lung diseases from High Resolution Computed Tomography (HRCT) images. this framework allows radiologists to remotely diagnose and share expert knowledge about lung HRCT interpretation, which is then used to develop a Computer Aided Diagnosis (CAD) system for lung disease. In this paper, we describe the knowledge acquisition system LMIK, which is Internet-based and platform-independent. the LMIK utilises the Internet to provide users with secure access to patient and research data and facilitates communication among highly qualified radiologists and researchers. It is currently used by five radiologists and over 20 researchers and has proved to be an invaluable research tool. Research is underway to develop computer algorithms for automatic diagnosis of lung diseases. In future, these algorithms will be integrated into LMIK to equip it with CAD capabilities to improve diagnostic accuracy of radiologists and extend availability of expert clinical knowledge to wider communities.

关键词： Computer Aided Diagnosis (CAD) DICOM High Resolution Computed Tomography (HRCT) knowledge acquisition medical imaging PACS telemedicine

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5th international Workshop on Multiple Classifier Systems, MCS 2004

5th International Workshop on Multiple Classifier Systems, M...

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5th international Workshop on Multiple Classifier Systems, MCS 2004

ISBN: (纸本)3540221441

the proceedings contain 37 papers. the special focus in this conference is on Bagging and Boosting. the topics include: Classifier ensembles for changing environments;classification and function estimation;boosting for noisy data;bagging decision multi-trees;a new approach to incremental learning;recursive ECOC learning machines;exact bagging with k-nearest neighbour classifiers;a maximum entropy approach;combining one-class classifiers to classify missing data;combining kernel information for support vector classification;combining classifiers using dependency-based product approximation with bayes error rate;combining dissimilarity-based one-class classifiers;a modular system for the classification of time series data;a probabilistic model using information theoretic measures for cluster ensembles;classifier fusion using triangular norms;dynamic integration of regression models;dynamic classifier selection by adaptive k-nearest-neighbourhood rule;spectral measure for multi-class problems;the relationship between classifier factorisation and performance in stochastic vector quantisation;a method for designing cost-sensitive ECOC;building graph-based classifier ensembles by random node selection;a comparison of ensemble creation techniques;multiple classifiers system for reducing influences of atypical observations;sharing training patterns among multiple classifiers;first experiments on ensembles of radial basis functions;an empirical bias-variance analysis;building diverse classifier outputs to evaluate the behavior of combination methods;an empirical comparison of hierarchical vs. two-level approaches to multiclass problems;experiments on ensembles with missing and noisy data;induced decision fusion in automated sign language interpretation and ensembles of classifiers derived from multiple prototypes and their application to handwriting recognition.

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