Exact(9)
We used substructures which have at least > 1% matches among the active dataset entries.
As the procedure outlined corresponds to supervised classification, all dataset entries are labeled.
The testing procedure consists of fetching the offline testing dataset entries and running the ANN for one epoch.
The fetched dataset is the offline training part appended to the previously collected dataset entries during past optimization rounds (
Thus, RSS has M rows and Nmax columns, where M is the number of dataset entries (i.e., the number of GSM band scans in the dataset).
For both scan sets, the total number of dataset entries is quite limited compared to the dimensionality of the RSS vectors.
Similar(51)
After every optimization round, the collected dataset entry is appended to the latest training dataset.
x i is the fingerprint dataset entry i, i.e., row i of RSS.
Then, a new collection procedure starts and the resulting dataset entry is appended to the fetched training dataset.
We define matrix RSS as the full observation matrix, whose element RSS ij is the strength value of carrier j in dataset entry i.
where α i and b are the parameters of the classifier, y i = ± 1 and x i are the class label and the fingerprint of dataset entry i (i.e., row i of RSS, RSS1, or RSS2 depending on the fingerprint used by the classifier), respectively, and K is the chosen kernel.
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