Sentence examples for multiple feature from inspiring English sources

The algorithm takes multiple feature interpretations of a part as input.

This paper pays attention to ensemble systems consisting of multiple feature extractors and multiple classifiers (MFMC).

Multiple feature interpretations and lack of scalability have been issues in the latest feature recognition research.

Moreover, multiple feature fusion is first used to model the various CMEs.

We present an algorithm that generates multiple feature interpretations within and across domains.

In this paper, we focus on the problem of extracting multiple feature models of a part.

In this architecture, a variety of Gabor features served as the multiple feature maps of the convolutional layer.

One possible solution to this problem is to use a multiple-classifier system (MCS) based on multiple feature representations.

To that end, we first formalize the notion of the MTRNN query by considering the influence of multiple feature types.

We define three types of multiple feature interpretations or models, (1) only positive, (2) mixed and (3) only negative.

These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models.