Online Bayesian Passive-Aggressive Learning

@inproceedings{Shi2014OnlineBP,
  title={Online Bayesian Passive-Aggressive Learning},
  author={Tianlin Shi and Jun Zhu},
  booktitle={Journal of Machine Learning Research},
  year={2014}
}
where the constant C = exp(ytτtμt xt + 12τ t xt xt) . Therefore, the distribution qt+1(w) = N (μt + τtxt, I) and as a by-product, the normalization term Γ(τt) = √ 2π K exp(τtytx > t μt + 1 2τ 2 t x > t xt). Next, we show that μt+1 = μt + τytxt is the optimal solution of the online Passive-Aggressive update rule (Crammer et al., 2006). To see this, we plug the derived Γ(τt) into (26), ignore constant terms and obtain 

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