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Pattern Classification Using a Penalized Likelihood Method

机译:使用惩罚似然法的模式分类

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Penalized likelihood is a well-known theoretically justified approach that has recently attracted attention by the machine learning society. The objective function of the Penalized likelihood consists of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, maximizing this objective function would lead to some sort of trade-off between the faithfulness and the smoothness of the fit. There has been a lot of research to utilize penalized likelihood in regression, however, it is still to be thoroughly investigated in the pattern classification domain. We propose to use a penalty term based on the K- nearest neighbors and an iterative approach to estimate the posterior probabilities. In addition, instead of fixing the value of K for all pattern, we developed a variable K approach, where the number of neighbors can vary from one sample to another. The chosen value of K for a given testing sample is influenced by the K values of its surrounding training samples as well as the most successful K value of all training samples. Comparison with a number of well-known classification methods proved the potential of the proposed method.
机译:惩罚可能性是一种众所周知的理论上合理的方法,最近已受到机器学习社会的关注。惩罚似然的目标函数包括数据的对数似然减去一些惩罚非光滑解的项。随后,最大化此目标函数将导致忠实度和拟合度之间进行某种折衷。在回归中利用罚分可能性进行了大量研究,但是,在模式分类领域仍需进行深入研究。我们建议使用基于K最近邻的惩罚项和一种迭代方法来估计后验概率。此外,我们没有固定所有模式的K值,而是开发了一种可变K方法,其中相邻样本的数量可以在一个样本之间变化。给定测试样本的K选择值受其周围训练样本的K值以及所有训练样本中最成功的K值影响。与许多知名分类方法的比较证明了该方法的潜力。

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