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Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014, Proceedings

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Pasi Fränti

    1. School of Computing, University of Eastern Finland, Joensuu, Finland

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  2. Gavin Brown

    1. School of Computer Science, The University of Manchester, Manchester, UK

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  3. Marco Loog

    1. Delft University of Technology, Delft, The Netherlands

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  4. Francisco Escolano

    1. Universidad de Alicante, Spain

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  5. Marcello Pelillo

    1. Università Ca’ Foscari Venezia, Venezia Mestre, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8621)

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

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Table of contents (47 papers)

  1. Front Matter

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  2. Graph Kernels

    1. A Graph Kernel from the Depth-Based Representation

      • Lu Bai, Peng Ren, Xiao Bai, Edwin R. Hancock
      Pages 1-11

    2. Incorporating Molecule’s Stereisomerism within the Machine Learning Framework

      • Pierre-Anthony Grenier, Luc Brun, Didier Villemin
      Pages 12-21

    3. Transitive State Alignment for the Quantum Jensen-Shannon Kernel

      • Andrea Torsello, Andrea Gasparetto, Luca Rossi, Lu Bai, Edwin R. Hancock
      Pages 22-31

  3. Clustering

    1. Balanced K-Means for Clustering

      • Mikko I. Malinen, Pasi Fränti
      Pages 32-41

    2. Poisoning Complete-Linkage Hierarchical Clustering

      • Battista Biggio, Samuel Rota Bulò, Ignazio Pillai, Michele Mura, Eyasu Zemene Mequanint, Marcello Pelillo et al.
      Pages 42-52

    3. A Comparison of Categorical Attribute Data Clustering Methods

      • Ville Hautamäki, Antti Pöllänen, Tomi Kinnunen, Kong Aik Lee, Haizhou Li, Pasi Fränti
      Pages 53-62

  4. Graph Edit Distance

    1. Improving Approximate Graph Edit Distance Using Genetic Algorithms

      • Kaspar Riesen, Andreas Fischer, Horst Bunke
      Pages 63-72

    2. Approximate Graph Edit Distance Guided by Bipartite Matching of Bags of Walks

      • Benoit Gaüzère, Sébastien Bougleux, Kaspar Riesen, Luc Brun
      Pages 73-82

    3. A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance

      • Andreas Fischer, Réjean Plamondon, Yvon Savaria, Kaspar Riesen, Horst Bunke
      Pages 83-92

  5. Graph Models and Embedding

    1. Flip-Flop Sublinear Models for Graphs

      • Brijnesh Jain
      Pages 93-102

    2. Node Centrality for Continuous-Time Quantum Walks

      • Luca Rossi, Andrea Torsello, Edwin R. Hancock
      Pages 103-112

    3. Max-Correlation Embedding Computation

      • Antonio Robles-Kelly
      Pages 113-122

  7. Combining and Selecting

    1. Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood

      • Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown
      Pages 143-152

    2. Majority Vote of Diverse Classifiers for Late Fusion

      • Emilie Morvant, Amaury Habrard, Stéphane Ayache
      Pages 153-162

  8. Joint Session

    1. Entropic Graph Embedding via Multivariate Degree Distributions

      • Cheng Ye, Richard C. Wilson, Edwin R. Hancock
      Pages 163-172

    2. On Parallel Lines in Noisy Forms

      • George Nagy
      Pages 173-182

  9. Metrics and Dissimilarities

    1. Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance

      • Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, Marco Loog
      Pages 183-192
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  1. Structural, Syntactic, and Statistical Pattern Recognition

Keywords

About this book

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014; comprising the International Workshop on Structural and Syntactic Pattern Recognition, SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The total of 25 full papers and 22 poster papers included in this book were carefully reviewed and selected from 78 submissions. They are organized in topical sections named: graph kernels; clustering; graph edit distance; graph models and embedding; discriminant analysis; combining and selecting; joint session; metrics and dissimilarities; applications; partial supervision; and poster session.

Editors and Affiliations

  • School of Computing, University of Eastern Finland, Joensuu, Finland

    Pasi Fränti

  • School of Computer Science, The University of Manchester, Manchester, UK

    Gavin Brown

  • Delft University of Technology, Delft, The Netherlands

    Marco Loog

  • Universidad de Alicante, Spain

    Francisco Escolano

  • Università Ca’ Foscari Venezia, Venezia Mestre, Italy

    Marcello Pelillo

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