论文信息 - Conditional Random Fields versus Hidden Markov Models for activity recognition in temporal sensor data

Conditional Random Fields versus Hidden Markov Models for activity recognition in temporal sensor data

Conditional Random Fields are a discriminative probabilistic model which recently gained popularity in applications that require modeling nonindependent observation sequences. In this work, we present the basic advantages of this model over generative models and argue about its suitability in the domain of activity recognition from sensor networks. We present experimental results on a realworld dataset that support this argumentation.

Ben Kröse | A. K. Noulas | T. L. M. van Kasteren

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