Continuous inference for sequence data

Details Continuous inference for sequence data Continuous inference for sequence data

2006-06-01

2009-06-23

Moran, Marjorie (Department: 1631)

Image analysis

Pattern recognition

Classification

C382S181000, C704S251000

Reexamination Certificate

active

07551784

ABSTRACT:
Dynamic inference is leveraged to provide online sequence data labeling. This provides real-time alternatives to current methods of inference for sequence data. Instances estimate an amount of uncertainty in a prediction of labels of sequence data and then dynamically predict a label when an uncertainty in the prediction is deemed acceptable. The techniques utilized to determine when the label can be generated are tunable and can be personalized for a given user and/or a system. Employed decoding techniques can be dynamically adjusted to tradeoff system resources for accuracy. This allows for fine tuning of a system based on available system resources. Instances also allow for online inference because the inference does not require knowledge of a complete set of sequence data.

REFERENCES:
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Fei et al. (Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'04), p. 1-12, 2004).
Aarino ( Speech recognition with Hidden Markov Models in Visual communication, Master of Science Thesis, University of Turkuu, p. 1-78, 1999).

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