A Place Personalized Semantic Recognition Method Based on Multi-Context Data and Cost-sensitive Integrated Model
A cost-sensitive, semantic recognition technology, applied in the field of place semantic recognition, which can solve problems such as poor model performance, failure to consider high context level place features, and insufficient training data.
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[0056] Example: such as figure 1 As shown, a place-based personalized semantic recognition method based on multi-context data and cost-sensitive integrated model, the method is divided into three stages: preprocessing, model training and semantic recognition, the specific steps are as follows:
[0057] The preprocessing stage realizes the functions of data preprocessing, feature extraction and cost matrix construction, which can be mainly divided into two parts: multi-context feature extraction and cost matrix construction:
[0058] The specific steps of multi-context feature extraction are as follows:
[0059] Step 1. All the access records v of the user in the same place form the visit record set V of the place, and V is regarded as a place in the identification.
[0060] Each access record can be expressed as v=(t in , t out , data), where t in and t out They are the start time and end time of the site visit respectively, and data is a collection of multi-context data....
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