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What is Semi-Supervised Learning

Chapter 20
Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture
Semi-supervised learning aims at labeling a set of unlabelled data with the help of a small set of labeled data.
Published in Chapter:
Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture
Hari Kishan Kondaveeti (VIT-AP University, Andhra Pradesh, India), Gonugunta Priyatham Brahma (VIT-AP University, Andhra Pradesh, India), and Dandhibhotla Vijaya Sahithi (VIT-AP University, Andhra Pradesh, India)
Copyright: © 2021 |Pages: 21
DOI: 10.4018/978-1-7998-1722-2.ch020
Abstract
Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.
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More Results
Chapter 4
Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT)
The algorithm is trained with incomplete data (labels or patterns) and the task is to identify output with the missing data, hence called as Semi-Supervised Learning.
Published in Chapter: Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT); From: Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
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Chapter 721
Semi-Supervised Dimension Reduction Techniques to Discover Term Relationships
Estimation of the parameters of a model considering both, un-labeled data and a small subset of labeled examples by human experts.
Published in Chapter: Semi-Supervised Dimension Reduction Techniques to Discover Term Relationships; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 20
Machine Learning
A method of empirical concept learning from both labeled and unlabeled data. A model created from a small amount of labeled data is used to classify unlabeled examples. These examples can then help during further learning improve the initial model.
Published in Chapter: Machine Learning; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 7
Machine Learning: A Revolution in Accounting
Semi-supervised learning is a machine learning approach that combines elements of both supervised and unsupervised methods. The model is trained on a dataset where some instances are labeled, while a larger portion remains unlabeled.
Published in Chapter: Machine Learning: A Revolution in Accounting; From: Artificial Intelligence Approaches to Sustainable Accounting
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Chapter 6
Machine Learning Approaches to Automated Medical Decision Support Systems
Combines the methodology of the supervised learning to process the labeled data with the unsupervised learning to compute the unlabeled data.
Published in Chapter: Machine Learning Approaches to Automated Medical Decision Support Systems; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 17
Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review
This type of machine learning method merges aspects of both supervised and unsupervised learning. It uses a combination of a small amount of labelled data and a large amount of unlabelled data to train models.
Published in Chapter: Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review; From: Big Data Analytics Techniques for Market Intelligence
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Chapter 3
Natural Language Processing in Online Reviews
It is a machine learning algorithm in which the machine learns from both labeled and unlabeled instances to build a model for predicting the class of unlabeled instances.
Published in Chapter: Natural Language Processing in Online Reviews; From: Natural Language Processing for Global and Local Business
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Chapter 44
Class Prediction in Test Sets with Shifted Distributions
machine learning technique that uses both labelled and unlabelled data for constructing the model.
Published in Chapter: Class Prediction in Test Sets with Shifted Distributions; From: Encyclopedia of Artificial Intelligence
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Chapter 1
Active Learning with SVM
The set of learning algorithms in which both labelled and unlabelled data in the training dataset are directly used to train the classifier.
Published in Chapter: Active Learning with SVM; From: Encyclopedia of Artificial Intelligence
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Chapter 142
Multi-Label Classification
Learning to label new data using both labeled training data plus unlabeled data.
Published in Chapter: Multi-Label Classification; From: Encyclopedia of Business Analytics and Optimization
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