Toward a Unified Science of Machine Learning
Toward a Unified Science of Machine Learning
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迈向机器学习的统一科学
DOI：
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发表时间：
1989
期刊：
影响因子：
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通讯作者：
P. Langley
中科院分区：
文献类型：
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作者：
P. Langley
Machine learning is a diverse discipline that acts as host to a variety of research goals, learning techniques, and methodological approaches. Researchers are making continual progress on all of these fronts tackling new problems, formulating innovative solutions to those problems, and devising new ways to evaluate their solutions. Such variety is the sign of a healthy and growing field. However, diversification also has its dangers. Subdisciplines can emerge that focus on one goal or evaluation scheme to the exclusion of others, and similarities among methods can be obscured by different notations and terminology. Thus, it is equally important to search for basic principles that unify the different paradigms within a field. Just as the twin forces of gravity and pressure hold a star in dynamic equilibrium while generating energy, so the joint processes of diversification and unification can hold a science together while fostering progress. In this editorial, I examine seven dichotomies that have emerged in recent years to partition the field of machine learning. I begin with three issues related to research goals and evaluation methodologies, then turn to four more substantive issues about learning methods themselves. In each case, I argue that long-term progress will occur only if we can find ways to unify these apparently competing views into a coherent whole.
