Sentence examples for extraction representation from inspiring English sources

Zhou L, Friedman C, Parsons S, Hripcsak G. System architecture for temporal information extraction, representation and reasoning in clinical narrative reports.

The papers were presented in eight sessions: e-government, network and load management, digital inclusion, knowledge extraction, representation and sharing, networks and communications, ICT applications for development, decision support, e-business and e-services, internet measurement.

A robust alignment of each new view is estimated with respect to the previously aligned data through fast extraction, representation and matching of feature points detected in overlapping areas from different views.

The temporal encoding schemes captured by the three orthogonal temporal factors provide alternative, though not completely independent, means of extraction, representation, and transmission of low repetition rate information.

Specifically, the first layer performs patch extraction and representation, which extracts overlapping patches from the input image and represents each patch as a high-dimensional vector.

The proposed method incorporates several steps, including partial sentence tree construction, derivation rule extraction, utterance representation in a vector space, the dialogue act set generation via spectral clustering, dialogue act representation using relative frequency weighted by normalized entropy, and finally a cosine distance measure between dialogue act and utterance.

In data mining and pattern classification, feature extraction and representation methods are a very important step since the extracted features have a direct and significant impact on the classification accuracy.

Obviously, this fact makes easier the knowledge extraction and representation tasks carried out when modeling real-world complex systems.

Our major contribution is the definition of an evolutionary optimization methodology that fully supports parameter adjustment of a multiscale shape descriptor for feature extraction and representation of leaf shapes in a high dimensional space.

Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery.

However, designing a fuzzy system is a thorny process, requiring many steps to be accomplished, from the knowledge extraction and representation, to the inference process, until the presentation of results.