Sentence examples for approaches for classifying from inspiring English sources

The phrase "approaches for classifying" is correct and usable in written English.
It can be used when discussing methods or strategies for categorizing or organizing information or items.
Example: "The research paper outlines several approaches for classifying different species of plants based on their characteristics."
Alternatives: "methods for categorizing" or "strategies for classifying".

Exact(11)

One of the main approaches for classifying the ECG signals is the use of wavelet transform.

We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks.

Pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM).

Using CARDIA data, we compared the validity of six approaches for classifying perimenopause status in order to better understand the performance of classification techniques which can be applied to general cohort data.

In this presented research legacy data was used to derive centroid values for World Reference Base Reference Soil Groups based on the Global Soil Map project defined soil properties in the specified depth intervals and test the applicability of numerical approaches for classifying soils, based on these specifications.

The main contribution of this paper is to propose a quantitative framework to understand the microscopic read-outs, while suggesting new approaches for classifying genes.

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Similar(49)

An approach for classifying operational, high and extreme wave events is presented.

A new approach for classifying users' search intentions is described in this paper.

We have proposed a constrained linear discriminant analysis (CLDA) approach for classifying the remotely sensed hyperspectral images.

FACS is the most used approach for classifying the facial muscle activity and correlating it with the emotions expressed by the analyzed subject [40 44].

As a first step towards addressing this, we introduce a machine learning approach for classifying distributed parallel computations based on communication patterns between compute nodes.

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