Given a training set of labeled samples, belonging to one or more categories, the category of the new unlabeled document is obtained using a similarity function.
This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data.
We iterate this process once and then retrain a final single classifier using all features from the original set of labeled examples and the new (predicted) set of labeled examples obtained during this iterative process.
Given a training set of labeled input objects { (x i, y i ) } i = 1 n, where each x ∈ X and y ∈ Y, the objective is to infer a relationship f : X → Y that minimizes some loss function.
In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines.
"The super-sized sixer is actually a set of labeled storage tanks that hold up to 688,200 gallons of beer," Mr. Stevens writes.
Supervised classification consists in learning a predictive model using a set of labeled samples.
We have a set of states and a set of labeled transitions between these states.
These learning strategies usually require a large set of labeled examples which can be expensive to obtain.
However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only.
These features, that we call supervised, are obtained by exploiting the information provided by an additional set of labeled images.
