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首页> 外文期刊>IEEE Signal Processing Magazine >Machine Learning Requires Probability and Statistics [Perspectives]
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Machine Learning Requires Probability and Statistics [Perspectives]

机译：机器学习需要概率和统计学[透视图]

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Machine Learning Requires Probability and Statistics The contemporary practice of machine learning often involves the application of deterministic, computationally intensive algorithms to iteratively minimize a criterion of fit between a discriminant and sample data. There is often little interest in using probability to model the uncertainty in the problem and statistics to characterize the behavior of predictors derived from data, with the emphasis being on computation and coding. It follows that little can be stated about performance on future data, beyond perhaps a simple error count on a given test set. In this article, we argue that the knowledge imparted by deterministic computational methods is not rigorously related to the real world and, in particular, future events. This connection requires rigorous probabilistic modeling and statistical inference as well as an understanding of the proper role of computation and an appreciation of epistemological issues.
机译：机器学习需要概率和统计，当代机器学习的实践往往涉及确定性，计算密集算法的应用，以迭代最小化判别和样本数据之间的拟合标准。在使用问题和统计数据中模拟不确定性的概率通常存在兴趣，以表征来自数据的预测器的行为，重点是计算和编码。因此，在未来数据的性能下可以说明很少，超过给定测试集的简单错误计数。在本文中，我们认为，由确定性计算方法传授的知识与现实世界严重相关，特别是未来的事件不严格相关。这一联系需要严格的概率建模和统计推断，并了解计算的适当作用和认识论问题的欣赏。

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