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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 1999"

共 16 条记录，以下是1-10 订阅

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learning in pattern recognition 1st

Learning in pattern recognition

引用

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

来源：评论

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Advances in predictive data mining methods 1st

Advances in predictive data mining methods

引用

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

来源：评论

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A data mining application for monitoring environmental risks 1st

A data mining application for monitoring environmental risks

引用

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

来源：评论

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Symbolic learning techniques in paper document processing 1st

Symbolic learning techniques in paper document processing

引用

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

来源：评论

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Reproductive process-oriented data mining from interactions between human and complex artifact system 1st

Reproductive process-oriented data mining from interactions ...

引用

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

作者： Sawaragi, T Kyoto Univ Grad Sch Engn Dept Precis Engn Kyoto 6068501 Japan

ISBN: (纸本)3540665994

This paper presents a method for concept formation of a personal learning apprentice (PLA) system that attempts to capture users' internal conceptual structure by observing interactions between user and system. Current hot topics on techniques of data mining may potentially contribute to the above purpose, but different from the conventional approaches of data mining, we have to consider more about the aspects in which hom the mined knowledge should be used by the human in the consequent processes, not only about what knowledge should be extracted. In this paper we propose such a process-oriented data mining method based upon an idea of soft systems methodologies proposed by P.B. Checkland in 1980's, and we propose an algorithm for its implementation using evolutional computing.

关键词： data mining

来源：评论

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Unsupervised learning of local mean gray values for image pre-processing 1st

Unsupervised learning of local mean gray values for image pr...

引用

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

作者： Jahn, H DLR Deutsch Zentrum Luft & Raumfahrt EV Inst Weltraumsensor & Planetenerkundung D-12489 Berlin Germany

ISBN: (纸本)3540665994

A parallel-sequential unsupervised learning method for image smoothing is presented which can be implemented with a Multi Layer Neural Network. In contrast to older work of the author which has used 4-connectivity of processing elements (neurons) leading to a very big number of recursions now each neuron of network lever t+1 is connected with (2M+1)*(2M+1) neurons of layer t guaranteeing a significant reduction of network layers with the same good smoothing results.

关键词： Neurons

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Automatic design of multiple classifier systems by unsupervised learning 1st

Automatic design of multiple classifier systems by unsupervi...

引用

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

作者： Giacinto, G Roli, F Univ Cagliari Dept Elect & Elect Engn I-09123 Cagliari Italy

ISBN: (纸本)3540665994

In the field of pattern recognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them make independent errors. This achievement pointed out the fundamental need for methods aimed to design ensembles of "independent" classifiers. However, the most of the recent work focused on the development of combination methods. In this paper, an approach to the automatic design of multiple classifier systems based on unsupervised learning is proposed. Given an initial set of classifiers, such approach is aimed to identify the largest subset of "independent" classifiers. A proof of the optimality of the proposed approach is given. Reported results on the classification of remote sensing images show that this approach allows one to design effective multiple classifier systems.

关键词： Unsupervised learning

来源：评论

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Multi-valued and universal binary neurons: learning algorithms, application to image processing and recognition 1st

Multi-valued and universal binary neurons: Learning algorith...

引用

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

作者： Aizenberg, Igor N. Aizenberg, Naum N. Krivosheev, Georgy A. Minaiskaya 28 kv. 49 Uzhgorod294015 Ukraine Paustovskogo 3 kv. 430 Moscow Russia

ISBN: (纸本)3540665994

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN and an arbitrary mapping described by partial-defined or fully-defined Boolean function (which can be not threshold) on the single UBN. The fast-converged learning algorithms are existing for both types of neurons. Such features of the MVN and UBN may be used for solution of the different kinds of problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN) for solution of the image processing and image analysis problems. Another effective application of the MVN is their use as the basic neurons in the neural networks oriented to the image recognition. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Neurons

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Non-hierarchical clustering with rival penalized competitive learning for information retrieval? 1st

Non-hierarchical clustering with rival penalized competitive...

引用

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

作者： King, Irwin Lau, Tak-Kan Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong

ISBN: (纸本)3540665994

In large content-based image database applications, efficient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques for text retrieval are well understood, efficient and robust indexing methodology for image retrieval is still in its infancy. In this paper, we present a non-hierarchical clustering scheme for index generation using the Rival Penalized Competitive learning (RPCL) algorithm. RPCL is a stochastic heuristic clustering method which provides good cluster center approximation and is computationally efficient. Using synthetic data as well as real data, we demonstrate the recall and precision performance measurement of nearest-neighbor feature retrieval based on the indexing structure generated by RPCL. © Springer-Verlag Berlin Heidelberg 1999.

关键词： stochastic systems

来源：评论

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Finding the Right Features for Instrument Classification of Classical Music 06

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

引用

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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