The phrase "approaches for pattern" is not correct and lacks clarity in written English.
It may be intended to refer to methods or strategies for identifying or analyzing patterns, but it needs additional context to be usable.
Example: "We need to explore various approaches for pattern recognition in our data analysis project."
Alternatives: "methods for pattern" or "strategies for pattern".
The system extracts features from voice recordings and considers an advanced statistical approach for pattern recognition.
This paper describes an approach for pattern recognition using genetic algorithm and general regression neural network (GRNN).
Artificial neural networks (ANN), have shown to be an effective, general-purpose approach for pattern recognition, classification, clustering, and prediction.
This interesting approach has been suggested by several authors including [ 46] or [ 48] and is often referred to as the "golden" approach for pattern problems.
Our results suggest that machine learning is a powerful and feasible approach for pattern recognition, culminating in a new integrated approach to disease classification for childhood arthritis that provides insight into the biology underlying clinical heterogeneity.
We have developed a complementary approach for pattern identification in non-sequence data with a largely overlapping range of applications, called discovery of all significant substructures (DASS) (Hollunder et al., 2007a).
The study by Olivier Soppera from theDépartement de Photochimie Génèrale and his colleagues at theInstitut de Chimie des Surfaces et Interfaces, Université de Haute-Alsace in France, has taken plasma polymerization to another level and demonstrated a new approach for patterning solid surfaces with reactive groups by utilizing the maleic anhydride pulsed plasma polymerization technique.
The paper introduces two acceleration approaches for fringe pattern processing.
We design and develop a shoe-integrated sensing system for objective bio-information collection, utilize signal processing algorithms for feature estimation and leverage machine learning as well as statistical analysis approaches for gait pattern examination.
