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As unlabeled database we used the GTZAN collection of music [27].
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Each learner then labels some unlabeled images in the database for the other learner.
It encodes the input data with a sparse representation in hidden layer and is trained using a large database of unlabeled images [29].
In our experiments, we utilized two music databases: one as unlabeled music data and the other for the actual supervised classification task.
We implemented this algorithm for the music genre classification task using two different databases: one as unlabeled data pool and the other as data for supervised classifier training.
However, there are abundant unlabeled sequence data in public databases such as UniProt [ 12].
On the other hand, there are abundant unlabeled sequence data in public databases such as UniProt [ 11].
But now machines can learn from the vast database to recognize similar, unlabeled objects, making possible a striking increase in recognition accuracy.
The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases.
As typical music identification algorithms usually identify unlabeled and distorted music fragments within the database, we also construct two query sets, i.e., Q S train and Q S test, for D B train and D B test, respectively.
First, the algorithm calculates fingerprints of the original music signals and stores them together with affiliated metadata into a fingerprint database; then, when presented with an unlabeled and probably distorted music segment, it extracts a fingerprint from the query audio and compares it with those in the database.
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