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- Supervised and semi-supervised training – We distinguish between supervised and semi-supervised training.
Then a risk-based tradeoff term between supervised and semi-supervised learning is integrated into the objective function of SSL.
Application of ssG to the data described in Section 2.1 permitted assessment of (i) the relationship between supervised and semi-supervised approaches to the genotyping task, (ii) the importance of quality filtering for genotyping accuracy and (iii) the differences between ssG and SNPscanner's model-based, supervised approach.
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The main difference between the supervised and semi-supervised K-associated graphs can be stated in relation to the set of neighbors, to which each vertex connects.
Our evaluation uses similar supervised and semi-supervised methods but includes many more unsupervised methods, distinguishes between experimental types and performs repeats, resulting in a more complete picture.
Similar to Table 3, both supervised and semi-supervised training algorithms are applied.
For each dataset, we compare supervised and semi-supervised training as previously mentioned.
Algorithms to detect anomalies generally fall into three types: unsupervised, supervised, and semi-supervised [1].
We assign different risk degrees to unlabeled data according to the different characteristics in supervised and semi-supervised learning.
Table 5 Performance of supervised and semi-supervised methods with 200 labeled segments Algorithms Spec ReliefF CS CCLS Ave.
Both supervised and semi-supervised techniques are least effective to large-scale data and are not scalable for being domain specific.
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