From machine learning to machine reasoning An essay
From machine learning to machine reasoning An essay
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DOI：
10.1007/s10994-013-5335-x
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发表时间：
2014-02-01
期刊：
影响因子：
7.5
通讯作者：
Bottou, Leon
中科院分区：
文献类型：
--
作者：
Bottou, Leon
“推理”的一个貌似合理的定义可能是“为了回答一个新问题，对先前获得的知识进行代数操作”。这个定义涵盖了一阶逻辑推理或概率推理。它还包括通常用于构建大型学习系统的更简单的操作。例如，我们可以通过首先使用适当的标记训练集训练字符分割器、孤立字符识别器和语言模型来构建光学字符识别系统。充分连接这些模块并微调结果系统可以被视为模型空间中的代数操作。由此产生的模型回答了一个新问题，即将文本页面的图像转换为计算机可读的文本。这一观察结果表明，在代数丰富的推理系统（如逻辑或概率推理）和简单的操作（如可训练学习系统的单纯连接）之间存在概念上的连续性。因此，与其试图弥合机器学习系统和复杂的“通用”推理机制之间的差距，我们可以代之以代数方式丰富适用于训练系统的操作集，并从头开始构建推理能力。
A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labelled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text.This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated "all-purpose" inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.