Corpus ID: 227054252

Quantum Multiple Kernel Learning

  title={Quantum Multiple Kernel Learning},
  author={Seyed Shakib Vedaie and Moslem Noori and Jaspreet Singh Oberoi and Barry C. Sanders and Ehsan Zahedinejad},
Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity of a machine learning model, referring to the ability of the model to approximate complex functions, has a significant influence on its performance in these tasks. One approach to enhancing the expressivity of kernel machines is to combine multiple individual kernels to arrive at a more expressive combined kernel… Expand


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In Advances in Neural Information Processing Systems
Bill Baird { Publications References 1] B. Baird. Bifurcation analysis of oscillating neural network model of pattern recognition in the rabbit olfactory bulb. In D. 3] B. Baird. Bifurcation analysisExpand
This article describes the process and findings of a community study that was part of a task force to improve educational experiences for new English learners, particularly the large number ofExpand