获得知识的正式框架:超越机器学习
Ola Hössjer1, Daniel Andrés Díaz-Pachón2, J Sunil Rao2
1Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一个数学框架,以精确定义学习和知识获取. 它使用主动信息量化信念,并通过要求信念形成的正确原因来区分学习和知识.
科学领域:
- 认识论的认识论学.
- 数学的逻辑数学逻辑
- 决策理论 决策理论
背景情况:
- 关于知识的哲学定义往往引用了合理的,真实的信念.
- 现有的框架缺乏学习和知识获取的精确数学定义.
- 信念通常被定性处理,阻碍了定量分析.
研究的目的:
- 开发一个精确的数学框架来定义学习和知识获取.
- 用一个新的指标来量化真实的信念的程度.
- 区分机器学习和真正的知识获取.
主要方法:
- 从贝叶斯规则中得出的认识论概率方面制定信念.
- 使用积极信息 (I+) 量化真实信念的程度.
- 引入一个平行世界框架来模型假设测试和参数估计.
主要成果:
- 学习被定义为真实信念的增加 (I+>0) 或虚假信念的减少 (I+<0).
- 获得知识需要学习正确的原因,涉及真实世界的参数估计.
- 该框架整合了频率主义和贝叶斯主义方法,适用于顺序设置.
结论:
- 拟议的框架提供了一种严格的混合方法来理解学习和知识.
- 它为分析和潜在改进机器学习算法提供了基础.
- 该模型阐明了单纯的学习和真正的知识获取之间的区别.
关键词:
贝叶斯的规则是贝叶斯的规则.积极的信息是积极的信息.相反的事实 (counterfactuals) 是指一个事实.认识论的概率概率.获取知识 获取知识学习,有道理的真实信念.复制研究是复制研究.更多相关视频
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