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Pattern Recognition and Data Mining

Third International Conference on Advances in Pattern Recognition, ICAR 2005, Bath, UK, August 22-25, 2005, Part I

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Sameer Singh

    1. Research School of Infomatics, Loughborough, UK

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  2. Maneesha Singh

    1. ATR Lab, Research School of Informatics, University of Loughborough, Loughborough, UK

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  3. Chid Apte

    1. IBM Corporation, New York, United States

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  4. Petra Perner

    1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3686)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: ICAPR 2005.

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Table of contents (74 papers)

  1. Front Matter

    Download chapter PDF

  2. Pattern Recognition and Data Mining

    1. Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies

      • Ghada Badr, B. John Oommen
      Pages 1-17

    2. Mathematical Features for Recognizing Preference in Sub-saharan African Traditional Rhythm Timelines

      • Godfried Toussaint
      Pages 18-27

    3. Empirical Bounds on Error Differences When Using Naive Bayes

      • Zoë Hoare
      Pages 28-34

    4. Effective Probability Forecasting for Time Series Data Using Standard Machine Learning Techniques

      • David Lindsay, Siân Cox
      Pages 35-44

    5. A Continuous Weighted Low-Rank Approximation for Collaborative Filtering Problems

      • Nicoletta Del Buono, Tiziano Politi
      Pages 45-53

    6. GP Ensemble for Distributed Intrusion Detection Systems

      • Gianluigi Folino, Clara Pizzuti, Giandomenico Spezzano
      Pages 54-62

    7. Clustered Trie Structures for Approximate Search in Hierarchical Objects Collections

      • R. Giugno, A. Pulvirenti, D. Reforgiato Recupero
      Pages 63-70

    8. On Adaptive Confidences for Critic-Driven Classifier Combining

      • Matti Aksela, Jorma Laaksonen
      Pages 71-80

    9. The RW2 Algorithm for Exact Graph Matching

      • Marco Gori, Marco Maggini, Lorenzo Sarti
      Pages 81-88

    10. Making Use of Unelaborated Advice to Improve Reinforcement Learning: A Mobile Robotics Approach

      • David L. Moreno, Carlos V. Regueiro, Roberto Iglesias, Senén Barro
      Pages 89-98

    11. Consolidated Trees: Classifiers with Stable Explanation. A Model to Achieve the Desired Stability in Explanation

      • Jesús M. Pérez, Javier Muguerza, Olatz Arbelaitz, Ibai Gurrutxaga, José I. Martín
      Pages 99-107

    12. Discovering Predictive Variables When Evolving Cognitive Models

      • Peter C. R. Lane, Fernand Gobet
      Pages 108-117

    13. Mathematical Morphology and Binary Geodesy for Robot Navigation Planning

      • F. Ortiz, S. Puente, F. Torres
      Pages 118-126

    14. Neural Network Classification: Maximizing Zero-Error Density

      • Luís M. Silva, Luís A. Alexandre, J. Marques de Sá
      Pages 127-135

    15. Taxonomy of Classifiers Based on Dissimilarity Features

      • Sarunas Raudys
      Pages 136-145

    16. Combination of Boosted Classifiers Using Bounded Weights

      • Hakan Altınçay, Ali Tüzel
      Pages 146-153

    17. Prediction of Commodity Prices in Rapidly Changing Environments

      • Sarunas Raudys, Indre Zliobaite
      Pages 154-163

    18. Develop Multi-hierarchy Classification Model: Rough Set Based Feature Decomposition Method

      • Qingdong Wang, Huaping Dai, Youxian Sun
      Pages 164-171

    19. On Fitting Finite Dirichlet Mixture Using ECM and MML

      • Nizar Bouguila, Djemel Ziou
      Pages 172-182
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Other volumes

  1. Pattern Recognition and Data Mining

  2. Pattern Recognition and Image Analysis

Editors and Affiliations

  • Research School of Infomatics, Loughborough, UK

    Sameer Singh

  • ATR Lab, Research School of Informatics, University of Loughborough, Loughborough, UK

    Maneesha Singh

  • IBM Corporation, New York, United States

    Chid Apte

  • Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

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