ArticleSIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining2017
Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.
Article in SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Improving the Accuracy of Progress Indication for Constructing Deep Learning Models.IEEE access : practical innovations, open solutions · 2022Article
- Progress Indication for Deep Learning Model Training: A Feasibility Demonstration.IEEE access : practical innovations, open solutions · 2020Article
- Progress Indication for Machine Learning Model Building: A Feasibility Demonstration.SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining · 2018Article
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Authors and funding
1 author.
Funding
Abstract
For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.