Sentence examples for using the extracted features from inspiring English sources

In this paper, the self-organizing map (SOM) proposed by Kohonen is used in order to classify the styles using the extracted features from "Feature extraction for art paintings" section.

In the model training phase, statistical models are created for each person using the extracted features.

For human classification, multiple humans and background moving objects are detected and classified using the extracted features.

Section 3 discusses the prediction of temporally localized distortions using the extracted features, followed by experimental results, and a discussion of the overall methodology.

By using the extracted features, an SVM classifier fused with probabilistic output is used to recognize the P-wave arrivals in the presence of noise.

In performing discriminative tasks in Big Data Analytics one can use Deep Learning algorithms to extract complicated nonlinear features from the raw data, and then use simple linear models to perform discriminative tasks using the extracted features as input.

Motion segmentation has been conducted by analyzing the degree of motion using the extracted feature vectors.

Finally, we use the extracted features to design decision trees as the base classifiers, and AdaBoost is used to obtain the final ensemble classifier.

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).

In this paper, we use the RBM to extract discriminative low-dimensional features from raw data with dimension up to 324 and then use the extracted features as the input of SVM for regression.

For each Result, Methodology, and Conclusion sentence in an abstract, these classifiers use the extracted features to predict whether it is strongly/weakly related to the Purpose sentence(s) (also high/low categories).