Artificial Neural Networks in Pattern Recognition
5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012, Proceedings
Published on 11. September 2012
X, 245 pages
978-3-642-33212-8 (ISBN)
Description
This book constitutes the refereed proceedings of the 5th INNS IAPR TC3 GIRPR International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 revised full papers presented were carefully reviewed and selected for inclusion in this volume. They cover a large range of topics in the field of neural network- and machine learning-based pattern recognition presenting and discussing the latest research, results, and ideas in these areas.
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Nadia Mana | Friedhelm Schwenker | Edmondo Trentin
Artificial Neural Networks in Pattern Recognition
5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012, Proceedings
Book
08/2012
Springer
€49.22
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Content
Learning Algorithms.- How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?- Kernel Robust Soft Learning Vector Quantization.- Incremental Learning by Message Passing in Hierarchical Temporal.- Representative Prototype Sets for Data Characterization and Classification.- Feature Selection by Block Addition and Block Deletion.- Gradient Algorithms for Exploration/Exploitation Trade-Offs: Global and Local Variants.- Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution to the Problem of Parameter Learning.- Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Structure Learning and an Empirical Evaluation.- Statistical Recognition of a Set of Patterns Using Novel Probability Neural Network.- On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognition.- Classification of Segmented Objects through a Multi-net Approach.- On Instance Selection in Audio Based Emotion Recognition.- Grayscale Images and RGB Video: Compression by Morphological Neural Network.- NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals.
