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Our supervised action recognition approach, outlined in Section 5, is based on SVM.
To ensure a fair comparison, we have applied our supervised filtering method to select 1024 features as well.
Our supervised learning framework exploits hundreds of time-varying features to capture changing network and diffusion patterns, content and sentiment information, timing signals, and user meta-data.
Hence, our choice is to rely on an unsupervised analysis which only needs raw data to build the features for our supervised classifier.
In our supervised learning experiments we compare the accuracy for two popular supervised learning algorithms: a Naive Bayes (NB) classifier and a support vector machine (SVM) classifier [28].
In our supervised recognition case, just like in many other computer vision tasks, features are histogram-based, and are not naturally represented in the space.
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In our approach, supervised learning is performed taking advantage of relevance labels provided by users.
On average 40% of alerts represent novel threats identified by our patented supervised machine learning models' extrapolation of existing threat knowledge," the company wrote on its website.
In our previous supervised unmixing work, objects were simply defined as contiguous pixel regions above a global threshold.
We also improved upon our previous supervised method by adjusting for the sampling bias of positive and negative training samples (iBMA-shortlist versus network A in Table 2).
Additional file 2: Table S3 gives the estimated regression coefficient and the posterior probability for each external data type in our revised supervised framework.
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