Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Analytical Mean Squared Error Curves for Temporal DifferenceLearning
Machine Learning
Neuro-Dynamic Programming
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Bayesian approaches to failure prediction for disk drives
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Modeling Multiple Time Series for Anomaly Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Real-time system for monitoring driver vigilance
IEEE Transactions on Intelligent Transportation Systems
Driving safety monitoring using semisupervised learning on time series data
IEEE Transactions on Intelligent Transportation Systems
A non-time series approach to vehicle related time series problems
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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This paper proposes a general framework for detecting unsafe states of a system whose basic real-time parameters are captured by multiple sensors. Our approach is to learn a dangerlevel function that can be used to alert the users of dangerous situations in advance so that certain measures can be taken to avoid the collapse. The main challenge to this learning problem is the labeling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points, where a definitive penalty can be assigned, and at the successful ends, where a certain reward can be assigned. In this paper, we treat the danger level as an expected future reward (a penalty is regarded as a negative reward) and use temporal difference (TD) learning to learn a function for approximating the expected future reward, given the current and historical sensor readings. The TD learning obtains the approximation by propagating the penalties/rewards observable at collapse points or successful ends to the entire feature space following some constraints. This avoids the labeling issue and naturally allows a general framework to detect unsafe states. Our approach is applied to, but not limited to, the application of monitoring driving safety, and the experimental results demonstrate the effectiveness of the approach.