2014 FromMachineLearningtoMachineRea

• (Bottou, 2014) ⇒ Léon Bottou. (2014). “From Machine Learning to Machine Reasoning.” In: Machine Learning Journal, 94(2). doi:10.1007/s10994-013-5335-x

Subject Headings: Automated Question Answering, Reasoning.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+From+Machine+Learning+to+Machine+Reasoning
• http://dl.acm.org/citation.cfm?id=2583611.2583675&preflayout=flat#citedby

Quotes

Abstract

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.

References

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• 2. Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen, ReasoNet: Learning to Stop Reading in Machine Comprehension, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2017, Halifax, NS, Canada
• 3. Hui Li, Yun Hu, Jun Shi, Using Social Network Information to Enhance Collaborative Filtering Performance, International Journal of Information and Communication Technology, v.8 n.4, p.315-327, January 2016
• 4. Usha B. Biradar, Harsha Gurulingappa, Lokanath Khamari, Shashikala Giriyan, Machine Learning Approach for Multi-Layered Detection of Chemical Named Entities in Text, International Journal of Software Science and Computational Intelligence, v.8 n.1, p.1-15, January 2016
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• 7. …

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 FromMachineLearningtoMachineRea	Léon Bottou			From Machine Learning to Machine Reasoning				10.1007/s10994-013-5335-x		2014