Artificial Neural Networks and Machine Learning – ICANN 2011

21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I

Editors:

ISBN: 978-3-642-21734-0 (Print) 978-3-642-21735-7 (Online)

Table of contents (47 chapters)

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  1. Front Matter

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    Pages 1-9

    Transformation Equivariant Boltzmann Machines

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    Pages 10-17

    Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines

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    Pages 18-25

    A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex

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    Pages 26-35

    1-Penalized Linear Mixed-Effects Models for BCI

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    Pages 36-43

    Slow Feature Analysis - A Tool for Extraction of Discriminating Event-Related Potentials in Brain-Computer Interfaces

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    Pages 44-51

    Transforming Auto-Encoders

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    Pages 52-59

    Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction

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    Pages 60-68

    Error-Backpropagation in Networks of Fractionally Predictive Spiking Neurons

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    Pages 69-76

    ESN Intrinsic Plasticity versus Reservoir Stability

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    Pages 77-84

    Adaptive Routing Strategies for Large Scale Spiking Neural Network Hardware Implementations

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    Pages 85-92

    Self-Organizing Map for the Multi-Goal Path Planning with Polygonal Goals

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    Pages 93-100

    Unlearning in the BCM Learning Rule for Plastic Self-organization in a Multi-modal Architecture

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    Pages 101-108

    Neuronal Projections Can Be Sharpened by a Biologically Plausible Learning Mechanism

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    Pages 109-116

    Explicit Class Structure by Weighted Cooperative Learning

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    Pages 117-125

    Unsupervized Data-Driven Partitioning of Multiclass Problems

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    Pages 126-133

    Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks

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    Pages 134-141

    An Improved Training Algorithm for the Linear Ranking Support Vector Machine

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    Pages 142-149

    Extending Tree Kernels with Topological Information

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    Pages 150-158

    Accelerating Kernel Neural Gas

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    Pages 159-166

    State Prediction: A Constructive Method to Program Recurrent Neural Networks

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