Sentence examples for patterns for extracting from inspiring English sources

The general focus is to apply Deep Learning algorithms to train the high-level data representation patterns based on a portion of the available input corpus, and then utilize the remaining input corpus with the learnt patterns for extracting the data abstractions and representations.

AutoPat parses each sentence in the test dataset once and compares with a finite set of unsupervised patterns for extracting gene regulation related sentences.

We are also planning on developing new rules and extend existing patterns for extracting more relations about TCMs.

Active contour models are widely used in pattern recognition for extracting an object shape.

After the unsupervised patterns have been established, these patterns are used for extracting the related regulation sentences from literatures in the interaction extraction modules.

Thus, we use completed local binary patterns (CLBPs) [49] for extracting local textural features, gray level co-occurrence matrix (GLCM) [50] statistics for representing global textures, and the curvelet transform [51] for shape description.

This study aims at providing a typology with four types of building patterns (e.g., collinear patterns, curvilinear patterns, parallel and perpendicular groups, and grid patterns) and presenting four integrated strategies for extracting these patterns effectively and efficiently.

The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images.

Data mining officially refers to one of the steps in the knowledge discovery process, namely "the application of specific algorithms for extracting patterns from data" (Idem, 39).

Broadly speaking, we aim to develop mathematically optimal procedures for extracting patterns and correlations from genetic databases.

This statistical modeling method, which collapses multivariate data into reduced dimensions, is useful for extracting patterns of co-variance across multiple observations.