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Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
Created by
Haebom
Author
Mehdi Hennequin, Abdelkrim Zitouni, Khalid Benabdeslem, Haytham Elghazel, Yacine Gaci
Outline
We extend the PAC-Bayesian framework to multiview learning by proposing a novel generalization bound based on the Renyi divergence. This provides an alternative to the existing Kullback-Leibler divergence-based bound, and extends the first- and second-order oracle PAC-Bayesian bounds and C-bound to the multiview setting. We design an efficient self-bound optimization algorithm for theoretical and practical applications.
Takeaways, Limitations
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Takeaways:
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We present a new approach to apply PAC-Bayesian theory to multi-view learning by proposing a new generalization boundary utilizing R enyi divergence.
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Extending the first- and second-order oracle PAC-Bayesian bounds and C-bound to multi-view environments to enhance theoretical depth.
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We present the possibility of practical application by developing an efficient self-boundary optimization algorithm that is consistent with theoretical results.
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Limitations:
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Only a summary of the paper's contents is presented, lacking information on specific performance evaluations, experimental results, and practical application cases.
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Lack of details on the advantages of R enyi divergence-based bounds and their comparison with the existing Kullback-Leibler divergence-based bounds.
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Lack of information about the computational complexity of the proposed algorithm and its performance on real-world datasets.
