What every computer scientist should know about floating-point arithmetic
ACM Computing Surveys (CSUR)
Theory and algorithms for hidden Markov models and generalized hidden Markov models
Theory and algorithms for hidden Markov models and generalized hidden Markov models
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Exploitation of Unlabeled Sequences in Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum Classification Error Training for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Network-based approach to online cursive script recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining residue contacts in proteins using local structure predictions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modular fuzzy-neuro controller driven by spoken language commands
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effect of silhouette quality on hard problems in gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary neural networks for anomaly detection based on the behavior of a program
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Using the Viterbi Path Along With HMM Likelihood Information for Online Signature Verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human action learning via hidden Markov model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research
Botnet traffic detection using hidden Markov models
Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research
P2P hierarchical botnet traffic detection using hidden Markov models
Proceedings of the 2012 Workshop on Learning from Authoritative Security Experiment Results
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Markov models are commonly used to analyze real-world problems. Their combination of discrete states and stochastic transitions is suited to applications with deterministic and stochastic components. Hidden Markov models (HMMs) are a class of Markov models commonly used in pattern recognition. Currently, HMMs recognize patterns using a maximum-likelihood approach. One major drawback with this approach is that data observations are mapped to HMMs without considering the number of data samples available. Another problem is that this approach is only useful for choosing between HMMs. It does not provide a criterion for determining whether or not a given HMM adequately matches the data stream. In this paper, we recognize complex behaviors using HMMs and confidence intervals. The certainty of a data match increases with the number of data samples considered. Receiver operating characteristic curves are used to find the optimal threshold for either accepting or rejecting an HMM description. We present one example using a family of HMMs to show the utility of the proposed approach. A second example using models extracted from a database of consumer purchases provides additional evidence that this approach can perform better than existing techniques.