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\refsection{0}
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      \field{journaltitle}{Mathematical Programming}
      \field{month}{8}
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      \field{title}{On the limited memory BFGS method for large scale optimization}
      \field{volume}{45}
      \field{year}{1989}
      \field{pages}{503\bibrangedash 528}
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    \entry{Dogleg}{inproceedings}{}{}
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        {IEEE Computer Society}%
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      \field{abstract}{In this paper, we introduce an advanced optimization algorithm for training feedforward neural networks. The algorithm combines the BFGS Hessian update formula with a special case of trust region techniques, called the Dogleg method, as an altenative technique to line search methods. Simulations regarding classification and function approximation problems are presented which reveal a clear improvement both in convergence and success rates over standard BFGS implementations.}
      \field{booktitle}{Neural Networks, IEEE - INNS - ENNS International Joint Conference on}
      \field{issn}{1098-7576}
      \field{month}{7}
      \field{title}{Training Feedforward Neural Networks with the Dogleg Method and BFGS Hessian Updates}
      \field{volume}{2}
      \field{year}{2000}
      \field{pages}{1138}
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    \entry{Numerical-Optimization-2006}{book}{}{}
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      \field{title}{Numerical Optimization}
      \field{year}{2006}
    \endentry
    \entry{BenchmarkTools}{article}{}{}
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      \field{eid}{arXiv:1608.04295}
      \field{eprintclass}{cs.PF}
      \field{eprinttype}{arXiv}
      \field{journaltitle}{arXiv e-prints}
      \field{month}{8}
      \field{title}{{Robust benchmarking in noisy environments}}
      \field{year}{2016}
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      \keyw{Computer Science - Performance,68N30,B.8.1,D.2.5}
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