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A typical HIV-1 protease cleavage sites prediction frame can be described as this: extract features from octapeptides, train a classifier based on the training samples, and then predict the label of a new unlabeled sample with the trained classifier.

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We call this operator general region assigned to binary (GRAB) and use this operator to extract features for facial recognition in images of varied scales.

To address this issue, we extract features not only from B-mode gray-scale ultrasound (US) images, but also from color Doppler energy (CDE) images.

The appearance-based technique which is implemented in this work to extract features is PCA, which is the earliest automated method proposed for face recognition.

For each Purpose sentence in an abstract, this classifier uses the extracted features to predict whether it is strongly/weakly related to the title (high/low categories).

The wind turbine gear-box fault experiment results proved that this method can effectively extract features from nonstationary signals, and can obtain excellent results despite of less training samples.

To deal with the problems mentioned above, in this paper we first extract features of both sequence and structure, and employ random forests [ 29] to generate an effective feature subset.

Discussion: This study shows that the extracted features from leaf images are invariant to scale and rotation because those features are close to positive correlation in terms of coefficient correlation.

We extract features from this data and apply a variety of machine learning algorithms, including Gaussian mixture models and Multi-task Multi-Kernel Leareing; we are currently working to apply Bayesian hierarchical multi-task learning and Deep Learning as well.

Multisensory information is used by this system in order to extract features that characterize the related driving session.

By doing this, it is possible to extract features from joint positions to alleviate the effects introduced by clothing changes or noise.

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