Dynamic across-time measurement interpretation

Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Boston, MA, July 1990
Dynamic across-time measurement interpretation
Dennis DeCoste
Abstract

Incrementally maintaining a qualitative understanding of physical system behavior based on observations is crucial to real-time process monitoring, diagnosis, and control.

This paper describes the DATMI theory for dynamically maintaining a pinterp-space, a concise representation of the local and global interpretations consistent with observations over time. Each interpretation signifies an alternative path of states in a qualitative envisionment.

DATMI can use domain-specific knowledge about state and transition probabilities to maintain the best working interpretation. By maintaining the space of alternative interpretations as well, DATMI avoids the need for extensive backtracking to handle incomplete or faulty data.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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