Knowledge discovery in databases: an overview
AI Magazine
Guest Editors‘ Introduction: On Applied Research in MachineLearning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Applying classification algorithms in practice
Statistics and Computing
On-board analysis of uncalibrated data for a spacecraft at mars
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple-Instance Regression with Structured Data
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
K-means in space: a radiation sensitivity evaluation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A process for predicting manhole events in Manhattan
Machine Learning
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
Machine Learning for theNew York City Power Grid
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative information acquisition for data-driven decisions
Machine Learning
Using random forests to diagnose aviation turbulence
Machine Learning
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The special issue on "Machine Learning for Science and Society" showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perform repairs on critical infrastructure, how we predict severe weather and aviation turbulence, how we conduct tax audits, whether we can detect privacy breaches in access to healthcare data, and how we link individuals across census data sets for new insights into population changes. In this introduction, we discuss the need for such a special issue within the context of our field and its relationship to the broader world. In the era of "big data," there is a need for machine learning to address important large-scale applied problems, yet it is difficult to find top venues in machine learning where such work is encouraged. We discuss the ramifications of this contradictory situation and encourage further discussion on the best strategy that we as a field may adopt. We also summarize key lessons learned from individual papers in the special issue so that the community as a whole can benefit.