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Updated: Apr 26, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Simple randomized algorithms for online learning with kernels.

Wenwu He1, James T Kwok2

  • 1Department of Mathematics and Physics, Fujian University of Technology, Fuzhou, Fujian 350118, China; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.

Neural Networks : the Official Journal of the International Neural Network Society
|August 10, 2014
PubMed
Summary
This summary is machine-generated.

We introduce two stochastic strategies to manage the support set size in kernel-based online learning, mitigating the curse of kernelization. These methods show sublinear expected regret and strong performance in experiments.

Keywords:
BudgetKernel methodsOnline learningStochastic strategies

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Deep Neural Networks for Image-Based Dietary Assessment
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Area of Science:

  • Machine Learning
  • Computer Science

Background:

  • Online learning with kernels faces challenges due to the curse of kernelization, which necessitates controlling the support set size.
  • Efficiently managing the support set budget is crucial for the scalability and performance of kernel-based online learning algorithms.

Purpose of the Study:

  • To propose novel stochastic strategies for effectively controlling the support set budget in kernel-based online learning.
  • To analyze the theoretical performance of the proposed strategies in terms of expected regret.

Main Methods:

  • Development of two simple and effective stochastic algorithms for budget control.
  • Theoretical analysis of the expected regret, demonstrating sublinear bounds relative to the learning horizon.

Main Results:

  • The proposed stochastic strategies offer effective control over the support set size.
  • Algorithms achieve expected regret that is sublinear in the horizon, indicating good scalability.
  • Experimental results on benchmark datasets show promising efficacy and efficiency.

Conclusions:

  • The introduced stochastic methods provide a practical solution for the curse of kernelization in online learning.
  • These strategies enhance both the performance and computational efficiency of kernel-based online learning systems.