Sentence examples for and classification into a from inspiring English sources

Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase.

Similar conclusions about the representations produced by HMAX follow from Experiment 1 with the CalTech stimuli, though as noted below under property 4, the use of such datasets and classification into a class of object such as animal versus non-animal does not capture the fundamental property 4 of encoding information about individual faces or objects, as contrasted with classes.

When class information is available, fusing the advantages of both clustering learning and classification learning into a single framework is an important problem worthy of study.

Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block.

This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body.

We have developed a method for integrating systematic genetics, high-throughput microscopy, image analysis and pattern classification into an automated data acquisition and analysis platform for cell biological screens in budding yeast (Chong et al. 2015).

Encouraged by the recent success of Convolutional Neural Network (CNN) architectures in image classification, we propose a novel resolution-aware deep model which combines convolutional image super-resolution and convolutional fine-grained classification into a single model in an end-to-end manner.

Aiming at making up for this deficiency, we designed a novel classification framework that performs unsupervised optimal feature selection (UOFS) to simultaneously integrate dimensionality reduction, sparse representation, jointly sparse feature extraction and feature selection as well as classification into a unified optimization objective.

Until now, 2501 serotypes have been described [ 8]; which turns Salmonella classification into a complex and laborious process in the clinical laboratory; therefore, several PCR based methods have recently been developed, and were reported to be a simple, highly sensitive, fast and reliable alternative when compared to traditional clinical laboratory methods [ 9, 10].

Workflow technology is involved to turn the overall image classification into a total automatic process.

It has been emphasized that deposits mainly reflect basal boundary zone processes (Carey 1991; Branney and Kokelaar 1997), and a classification into granular, tractional, and direct fallout-dominated boundary flows has been adopted by Branney and Kokelaar (2002, Chap. 4).