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Artificial Neural Networks in Pattern Recognition

8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19–21, 2018, Proceedings

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Luca Pancioni

    1. University of Siena, Siena, Italy

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  2. Friedhelm Schwenker

    1. Ulm University, Ulm, Germany

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  3. Edmondo Trentin

    1. University of Siena, Siena, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11081)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ANNPR 2018.

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Table of contents (31 papers)

  1. Front Matter

    Pages I-XI
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  2. Invited Papers

    1. Front Matter

      Pages 1-1
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    2. What’s Wrong with Computer Vision?

      • Marco Gori
      Pages 3-16

    3. Deep Learning in the Wild

      • Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi et al.
      Pages 17-38

  3. Learning Algorithms and Architectures

    1. Front Matter

      Pages 39-39
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    2. Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines

      • Shigeo Abe
      Pages 41-53

    3. DLL: A Fast Deep Neural Network Library

      • Baptiste Wicht, Andreas Fischer, Jean Hennebert
      Pages 54-65

    4. Selecting Features from Foreign Classes

      • Ludwig Lausser, Robin Szekely, Viktor Kessler, Friedhelm Schwenker, Hans A. Kestler
      Pages 66-77

    5. A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs

      • Vincenzo Laveglia, Edmondo Trentin
      Pages 78-89

    6. Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text

      • Andrea Zugarini, Jérémy Morvan, Stefano Melacci, Stefan Knerr, Marco Gori
      Pages 90-101

    7. SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation

      • Abel Zacarias, Luís A. Alexandre
      Pages 102-112

    8. Classification Uncertainty of Deep Neural Networks Based on Gradient Information

      • Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk
      Pages 113-125

    9. Learning Neural Models for End-to-End Clustering

      • Benjamin Bruno Meier, Ismail Elezi, Mohammadreza Amirian, Oliver Dürr, Thilo Stadelmann
      Pages 126-138

    10. A \(k\)-Nearest Neighbor Based Algorithm for Multi-Instance Multi-Label Active Learning

      • Adrian T. Ruiz, Patrick Thiam, Friedhelm Schwenker, Günther Palm
      Pages 139-151

    11. Manifold Learning Regression with Non-stationary Kernels

      • Alexander Kuleshov, Alexander Bernstein, Evgeny Burnaev
      Pages 152-164

    12. F-Measure Curves for Visualizing Classifier Performance with Imbalanced Data

      • Roghayeh Soleymani, Eric Granger, Giorgio Fumera
      Pages 165-177

    13. Maximum-Likelihood Estimation of Neural Mixture Densities: Model, Algorithm, and Preliminary Experimental Evaluation

      • Edmondo Trentin
      Pages 178-189

    14. Generating Bounding Box Supervision for Semantic Segmentation with Deep Learning

      • Simone Bonechi, Paolo Andreini, Monica Bianchini, Franco Scarselli
      Pages 190-200

    15. Inductive–Transductive Learning with Graph Neural Networks

      • Alberto Rossi, Matteo Tiezzi, Giovanna Maria Dimitri, Monica Bianchini, Marco Maggini, Franco Scarselli
      Pages 201-212

    16. Bounded Rational Decision-Making with Adaptive Neural Network Priors

      • Heinke Hihn, Sebastian Gottwald, Daniel A. Braun
      Pages 213-225Open Access
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About this book

This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018.


The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. 


Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Editors and Affiliations

  • University of Siena, Siena, Italy

    Luca Pancioni, Edmondo Trentin

  • Ulm University, Ulm, Germany

    Friedhelm Schwenker

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