On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Learning Effects of Robot Actions Using Temporal Associations
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum Classification Error Training for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining temporal patterns of movement for video content classification
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Data & Knowledge Engineering
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Mining relationships among interval-based events for classification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
Learning to recognize agent activities and intentions
Learning to recognize agent activities and intentions
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This paper shows how to learn general, Finite State Machine representations of activities that function as recognizers of previously unseen instances of activities. The central problem is to tell which differences between instances of activities are unimportant and may be safely ignored for the purpose of learning generalized representations of activities. We develop a novel way to find the "essential parts" of activities by a greedy kind of multiple sequence alignment, and a method to transform the resulting alignments into Finite State Machine that will accept novel instances of activities with high accuracy.