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What is Gradient Boosting

Chapter 8
Examining Fractal Image Processing and Analysis
Gradient boosting is a machine learning technique for regression and classification problems, which produces a strong classifier in the form of an ensemble of weak classifiers. Gradient boosting combines weak classifiers in iteratively. Gradient boosting generalizes by minimizing loss function and loss function must be differentiable. Gradient boosting involves weak classifiers, a loss function that has to be minimized and an additive model to add weak classifiers to minimize loss function.
Published in Chapter:
Fatigue Monitoring for Drivers in Advanced Driver-Assistance System
Lakshmi Sarvani Videla (Koneru Lakshmaiah Education Foundation, India) and M. Ashok Kumar P (Koneru Lakshmaiah Education Foundation, India)
Copyright: © 2020 |Pages: 18
DOI: 10.4018/978-1-7998-0066-8.ch008
Abstract
The detection of person fatigue is one of the important tasks to detect drowsiness in the domain of image processing. Though lots of work has been carried out in this regard, there is a void of work shows the exact correctness. In this chapter, the main objective is to present an efficient approach that is a combination of both eye state detection and yawn in unconstrained environments. In the first proposed method, the face region and then eyes and mouth are detected. Histograms of Oriented Gradients (HOG) features are extracted from detected eyes. These features are fed to Support Vector Machine (SVM) classifier that classifies the eye state as closed or not closed. Distance between intensity changes in the mouth map is used to detect yawn. In second proposed method, off-the-shelf face detectors and facial landmark detectors are used to detect the features, and a novel eye and mouth metric is proposed. The eye results obtained are checked for consistency with yawn detection results in both the proposed methods. If any one of the results is indicating fatigue, the result is considered as fatigue. Second proposed method outperforms first method on two standard data sets.
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Chapter 8
Impact of Machine Learning and Deep Learning Techniques in Autism
It is a ML method used in regression and classification applications. It provides an ensemble of weak models for predictions, often decision trees, as a prediction model.
Published in Chapter: Impact of Machine Learning and Deep Learning Techniques in Autism; From: Future of AI in Medical Imaging
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Chapter 8
Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services
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Chapter 19
Crime Hotspot Prediction Using Big Data in China
An algorithm that selects the direction of gradient descent during iteration to ensure the best results.
Published in Chapter: Crime Hotspot Prediction Using Big Data in China; From: Handbook of Research on Managerial Practices and Disruptive Innovation in Asia
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