Sentence examples for representations from a large from inspiring English sources

"In advance of the budget he routinely considers a wide range of representations from a large number of people".

The key feature of the method is learning word representations from a large amount of text data for feature extraction.

The system learns better feature representations from a large collection of PubMed text and uses a regularized linear classifier during the supervised training on annotated data.

Supervision transfer enables learning of rich representations from a large labelled modality as a supervisory signal for training representations for a new unlabelled paired modality and can be used as a pre-training procedure for new modalities with limited labelled data.

A two-step boosting feature selection (BFS) technique is proposed for selecting a compact and discriminant user-specific feature representation from a large number of feature extractions.

The proposed method includes Natural Language Processing NLPP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification.

The proposed method includes NLP tasks for text preprocessing and learning word representation features from a large amount of raw text data, in addition to the word, the word n-gram, the character n-gram, and the traditional orthographic information (baseline features) for feature extraction, and applies CRF for token labeling.

The approaches in the second research direction induce better feature representation by learning from a large unlabeled corpus.

Benefits of using data from the EIP program include laboratory confirmation of influenza and representation of a large population from geographically diverse areas in the United States.

However, most of the related studies mainly focus on learning features and representations from very large scale dataset relying on deep network architecture, which is doomed to fail with limited training samples due to its high complexity.

The ability of Deep Learning to extract high-level, complex abstractions and data representations from large volumes of data, especially unsupervised data, makes it attractive as a valuable tool for Big Data Analtyics.