ExtraFerns: Fully Parallel Ensemble Learning Technique with Non-Greedy yet Minimal Memory Access Training

DM techniques DM techniques give helpful info from the historical comes counting on that the hiring-manager will build selections for recruiting high-quality force, by applying K-means and mathematical logic algorithms. huge information analytics in hiring and the way it will assist you recruit prime talent, "Big information is that the way forward for recruiting, however you cannot simply information mine your thanks to the privilege candidate, “Big info to alter your accomplishment system. What’s certain is that big info is that the fate of occupation choosing and advancement, Associate in Nursing seeing a way to know it are going to be basic to an organization's prosperity. Nowadays, vast info helps quickly developing organizations find their ideal specialists, designers and officers. an enormous information platform utilizing prophetic analytics and machine learning for quick, accurate, and straightforward candidate rummage around for recruiters. During this paper a data-mining framework supported Associate in nursing ensemble-learning technique to refocus on the factors for personnel. On-line job boards are employed by scores of job seekers, UN agency flick through the postings for jobs that match their interest. Queries are crafted victimization word generated by the users, which cannot match the language employed in the work postings.

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Facial Expression Recognition using Ensemble Learning Technique

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4508.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 10274-10278

Keyword(s):

Facial Expression ◽

Ensemble Learning ◽

Facial Expression Recognition ◽

Learning Algorithms ◽

Predictive Performance ◽

Recognition System ◽

Training Dataset ◽

Expression Recognition ◽

Ensemble Technique ◽

Learning Technique

The most natural, influential and powerful way to communicate or convey a message is face expressions. In the field of computer engineering, facial expression recognition system, is helpful in areas like healthcare system, computer graphics, biometric devices, mobile phones, etc. Technologies such as virtual reality (VR) and augmented reality (AR) make use of facial expression recognition to implement a natural, friendly communication with humans. In this paper an approach for Facial Expression Recognition using Ensemble Learning Technique has been proposed. Ensemble methods use various learning algorithms to obtain good predictive performance that could be obtained from any of the basic learning algorithms alone. In the proposed method, initially the features are extracted from static images using color histograms. This process is done for all images gathered in the training dataset. The ensemble technique is then applied on the featured dataset in order to categorize a given image into one of the six emotions, happy, sad, fear, angry, disgust, and surprise. A satisfactory result has been obtained using static image dataset taken from kaggle and uci machine learning repository

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Does Ensemble Learning Always Lead to Better Forecasts?

Applied Economics and Finance ◽

10.11114/aef.v7i2.4716 ◽

2020 ◽

Vol 7 (2) ◽

pp. 51

Author(s):

Hitoshi Hamori ◽

Shigeyuki Hamori

Keyword(s):

Machine Learning ◽

Economic Analysis ◽

Ensemble Learning ◽

Majority Voting ◽

Machine Learning Technique ◽

Learning Technique ◽

Individual Classifier

Ensemble learning is a common machine learning technique applied to business and economic analysis in which several classifiers are combined using majority voting for better forecasts as compared to those of individual classifier. This study presents a counterexample, which demonstrates that ensemble learning leads to worse classifications than those from individual classifiers, using two events and three classifiers. If there is an outstanding classifier, we should follow its forecast instead of using ensemble learning.

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