Exact(60)
Results for the context recognition are presented in Section 6.1.
Figure 8 Context recognition performance as a function of used segment length.
(7) To apply the algorithm in the context of context recognition.
Context recognition was performed using the method presented in Section 4.1.
Indeed, they seem to be less applied than supervised learning in the domain of context recognition.
We achieve this by implementing a first stage for audio context recognition and event set selection.
This is due to the 9% error in the context recognition step.
There are a wide range of algorithms and models for supervised learning and context recognition.
This has led to the development of the Context Recognition Network [15].
Context recognition algorithms based on supervised and unsupervised learning methods primarily use probabilistic and statistical reasoning.
This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.
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