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Deep neural-kernel blocks.

Siamak Mehrkanoon1

  • 1Department of Data Science and Knowledge Engineering, Maastricht University, The Netherlands.

Neural Networks : the Official Journal of the International Neural Network Society
|April 22, 2019
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Summary
This summary is machine-generated.

This study presents new deep learning models combining neural networks and kernel methods. These hybrid models with novel pooling layers improve performance on real-world data.

Keywords:
Competitive learningDeep learningDimensionality reductionKernel methodsNeural networksPooling layer

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Traditional kernel-based models and neural networks have limitations in capturing complex data patterns.
  • Hybrid approaches offer potential for enhanced representational power.

Purpose of the Study:

  • To introduce novel deep architectures integrating neural and kernel methodologies.
  • To explore the efficacy of different pooling layers within a hybrid framework.

Main Methods:

  • Development of hybrid neural-kernel core models.
  • Implementation and examination of average, maxout, and convolutional pooling layers.
  • Integration of these pooling layers into deep architectures.

Main Results:

  • The proposed hybrid models demonstrate improved performance compared to standalone deep hybrid or kernel models.
  • Pooling layers effectively reduce dimensionality and foster sub-network formation.
  • Enhanced feature projection through pointwise convolutional layers.

Conclusions:

  • Hybrid neural-kernel architectures with advanced pooling offer superior performance on real-life datasets.
  • The novel pooling strategies contribute significantly to model effectiveness.
  • This work advances hybrid deep learning methodologies.