Deep Kernel Machines: Synergistic Representation and Margin Optimization for Robust Generalization Article Swipe

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Revista, Zen , IA, 10 · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5281/zenodo.17818095

Deep learning has achieved unprecedented success in learning complex representations from high-dimensional data, revolutionizing fields from computer vision to natural language processing. However, despite their empirical prowess, deep neural networks often lack strong theoretical generalization guarantees, can be susceptible to adversarial perturbations, and may exhibit brittle behavior on out-of-distribution samples. In contrast, kernel methods, particularly Support Vector Machines, are celebrated for their solid theoretical foundations rooted in statistical learning theory, offering explicit margin maximization for robust generalization. This paper introduces Deep Kernel Machines (DKMs), a novel framework that synergistically integrates the representation learning capabilities of deep neural networks with the robust generalization principles of kernel methods. DKMs are designed to jointly optimize both the hierarchical feature extraction process and the large-margin decision boundary in an end-to-end fashion. By embedding a kernel-based classifier or regressor directly into the deep learning architecture and optimizing a composite objective function, DKMs aim to learn representations that are not only highly discriminative but also well-conditioned for maximal margin separation. This approach inherently promotes enhanced robustness against perturbations and superior generalization performance across diverse datasets. We detail the architectural considerations, training methodology, and mathematical formulation of DKMs, providing a comprehensive theoretical and empirical justification for their effectiveness in achieving robust generalization.

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article

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https://doi.org/10.5281/zenodo.17818095

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https://openalex.org/W7108721070

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https://openalex.org/W7108721070

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https://doi.org/10.5281/zenodo.17818095

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Deep Kernel Machines: Synergistic Representation and Margin Optimization for Robust Generalization

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article

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2025

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2025-12-04

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Revista, Zen, IA, 10

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https://doi.org/10.5281/zenodo.17818095

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green

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https://doi.org/10.5281/zenodo.17818095

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Concepts

Artificial intelligence, Computer science, Deep learning, Machine learning, Generalization, Robustness (evolution), Margin (machine learning), Discriminative model, Kernel method, Artificial neural network, Deep neural networks, Decision boundary, Feature learning, Kernel (algebra), Classifier (UML), Embedding, Maximization, Support vector machine, Representation (politics), Deep belief network, Process (computing), Feature (linguistics), Robust optimization, Supervised learning, Relevance vector machine, Feature extraction, Pattern recognition (psychology), Empirical risk minimization, Perceptron, Norm (philosophy)

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