适应性实验机器学习可以帮助科学发现

Cheng Soon Ong
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引用次数: 0

摘要

机器学习依靠数学、数据和算法来识别自然现象中的模式。换句话说,计算机在没有明确指示如何做的情况下逐渐提高其准确性。机器学习是人工智能的一个子集,可以定义为计算机程序从特定类型的数据和性能度量方面的经验中学习的能力。传统的学习过程首先是将输入数据输入到算法中,算法通过提供预测输出来提取特征并对其进行分类。我们参考了传统的机器自适应实验,机器学习可以帮助科学发现
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive experiments Machine learning can help scientific discovery
Machine learning relies on mathematics, data, and algorithms to identify patterns in natural phenomena. In other words, computers gradually improve their accuracy without being explicitly instructed how to do so. Machine learning is a subset of artificial intelligence which can be defined as the ability of a computer program to learn from experience with respect to a specific kind of data and a performance measure. The conventional learning process begins by feeding input data to the algorithm, which extracts features and classifies them by providing a predicted output. We refer to the conventional machine Adaptive experiments Machine learning can help scientific discovery
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