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1st international workshop on pattern recognition (IWPR)

作者： Birjandtalab, Javad Pouyan, Maziyar Baran Nourani, Mehrdad Univ Texas Richardson Qual Life Technol Lab Richardson TX 75080 USA

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

关键词： EEG signals unsupervised seizure detection feature extraction data visualization

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Finding the Right Features for Instrument Classification of Classical Music 06

<bold>Finding the Right Features for Instrument Classificati...

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1st international workshop international AI and data mining

作者： Deng, Da Simmermacher, Christian Cranefield, stephen Univ Otago Dept Informat Sci POB 56 Dunedin New Zealand

ISBN: (纸本)9780769527307

In tackling data mining and pattern recognition tasks, finding a compact but effective set of features is often a crucial step in the whole problem solving process. In this paper we present an empirical study on feature selection for classical instrument recognition, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes in terms of their classification performance. It is revealed that there is significant redundancy in existing feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary for optimising feature selection for the instrument recognition problem.

关键词： data mining

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Automatic annotation of protein functional class from sparse and imbalanced data sets

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1st international workshop on data mining and Bioinformatics

作者： Jung, Jaehee Thon, Michael R. Texas A&M Univ Dept Comp Sci College Stn TX 77843 USA Texas A&M Univ Dept Plant Pathol & Microbiol College Stn TX 77843 USA

ISBN: (纸本)9783540689706

In recent years, high-throughput genome sequencing and sequence analysis technologies have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common controlled vocabulary for describing gene function however the process for annotating proteins with GO terms is usually through a tedious manual curation process by trained professional annotators. With the wealth of genomic data that are now available, there is a need for accurate automated annotation methods. In this paper, we propose a method for automatically predicting GO terms for proteins by applying statistical pattern recognition techniques. We employ protein functional domains as features and learn independent Support Vector machine classifiers for each GO term. This approach creates sparse data sets with highly imbalanced class distribution. We show that these problems can be overcome with standard feature and instance selection methods. We also present a meta-learning scheme that utilizes multiple SVMs trained for each GO term, resulting in improved overall performance than either SVM can achieve alone. The implementation of the tool is available at http://***/AAPFC.

关键词： gene annotation feature selection gene ontology InterPro imbalanced data

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A fast SVM training algorithm

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international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2003年第3期17卷 367-384页

作者： Dong, JX Suen, CY Concordia Univ Ctr Pattern Recognit & Machine Intelligent Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci Montreal PQ H3G 1M8 Canada

A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel caching plays a key role in reducing the number of kernel evaluations by maximal reusage of cached kernel elements. Extensive experiments have been conducted on a large handwritten digit database MNIst to show that the proposed algorithm is much faster than Keerthi et al.'s improved SMO, about nine times. Combined with principal component analysis, the total training for ten one-against-the-rest classifiers on MNIst took less than an hour. Moreover, the proposed fast algorithm speeds up SVM training without sacrificing the generalization performance. The 0.6% error rate on MNIst test set has been achieved. The promising scalability of the proposed scheme paves a new way to solve more large-scale learning problems in other domains such as data mining.

关键词： support vector machine kernel caching working set selection digest shrinking handwritten digit recognition

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A fast SVM training algorithm

A fast SVM training algorithm

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1st international workshop on pattern recognition with Support Vector machines

作者： Dong, JX Suen, CY Concordia Univ Ctr Pattern Recognit & Machine Intelligent Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci Montreal PQ H3G 1M8 Canada

关键词： support vector machine kernel caching working set selection digest shrinking handwritten digit recognition

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Typhoon analysis and data mining with kernel methods 1st

Typhoon analysis and data mining with kernel methods

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1st international workshop on pattern recognition with Support Vector machines

作者： Kitamoto, A Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

ISBN: (纸本)354044016X

The analysis of the typhoon is based on the manual pattern recognition of cloud patterns on meteorological satellite images by human experts, but this process may be unstable and unreliable, and we think could be improved by taking advantage of both the large collection of past observations and the state-of-the-art machine learning methods, among which kernel methods, such as support vector machines (SVM) and kernel PCA, are the focus of the paper. To apply the "learning-from-data" paradigm to typhoon analysis, we built the collection of more than 34,000 well-framed typhoon images to be used for spatio-temporal data mining of typhoon cloud patterns with the aim of discovering hidden and unknown regularities contained in large image databases. In this paper, we deal with the problem of visualizing and classifying typhoon cloud patterns using kernel methods. We compare preliminary results with baseline algorithms, such as principal component analysis and a k-NN classifier, and discuss experimental results with the future direction of research.

关键词： Principal component analysis

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learning in pattern recognition 1st

Learning in pattern recognition

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1st international workshop on machine learning and data mining in pattern recognition

作者： Petrou, M Univ Surrey Sch Elect Engn Informat Technol & Math Guildford GU2 5XH Surrey England

learning in the context of a pattern recognition system is defined as the process that allows it to cope with real and ambiguous data. The various ways by which artificial decision systems operate are discussed in con... 详细信息

ISBN: (纸本)3540665994

关键词： Artificial intelligence

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Symbolic learning techniques in paper document processing 1st

Symbolic learning techniques in paper document processing

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1st international workshop on machine learning and data mining in pattern recognition, mldm 1999

作者： Altamura, Oronzo Esposito, Floriana Lisi, Francesca A. Malerba, Donato Dipartimento di Informatica Università degli Studi di Bari via Orabona 4 Bari70126 Italy

ISBN: (纸本)3540665994

WISDOM++ is an intelligent document processing system that transforms a paper document into HTML/XML format. The main design requirement is adaptivity, which is realized through the application of machine learning methods. This paper illustrates the application of symbolic learning algorithms to the first three steps of document processing, namely document analysis, document classification and document understanding. machine learning issues related to the application are: Efficient incremental induction of decision trees from numeric data, handling of both numeric and symbolic data in first-order rule learning, learning mutually dependent concepts. Experimental results obtained on a set of real-world documents are illustrated and commented. © Springer-Verlag Berlin Heidelberg 1999.

关键词： data handling

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A data mining application for monitoring environmental risks 1st

A data mining application for monitoring environmental risks

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1st international workshop on machine learning and data mining in pattern recognition

作者： Scaringella, A Consiglio Minist DSTN Serv Idrograf & Mareograf Nazl I-00185 Rome Italy Univ Rome La Sapienza CATTID Rome Italy

ISBN: (纸本)3540665994

We describe the guidelines of a system for monitoring environmental risk situations. The system is based on data mining techniques and in particular classification trees working on the data base collected by the Italian National Hydro-geological Net. The gear of our application is to achieve a better discrimination among cases then that obtained by the system which is presently in use. The decision trees are evaluated and selected via a metric that takes a weighted account of the errors of different kinds.

关键词： Decision trees

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Advances in predictive data mining methods 1st

Advances in predictive data mining methods

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1st international workshop on machine learning and data mining in pattern recognition, mldm 1999

作者： Hong, Se June Weiss, Sholom M. IBM T.J. Watson Research Center P.O. Box 218 Yorktown HeightsNY10598 United States

ISBN: (纸本)3540665994

Predictive models have been widely used long before the development of the new field that we call data mining. Expanding application demand for data mining of ever increasing data warehouses, and the need for understandability of predictive models with increased accuracy of prediction, all have fueled recent advances in automated predictive methods. We first examine a few successful application areas and technical challenges they present. We discuss some theoretical developments in PAC learning and statistical learning theory leading to the emergence of support vector machines. We then examine some technical advances made in enhancing the performance of the models both in accuracy (boosting, bagging, stacking) and scalability of modeling through distributed model generation. © Springer-Verlag Berlin Heidelberg 1999.

关键词： data mining

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