A Novel Manifold Regularized Online Semi-supervised Learning Model

2017 ◽  
Vol 10 (1) ◽  
pp. 49-61 ◽  
Author(s):  
Shuguang Ding ◽  
Xuanyang Xi ◽  
Zhiyong Liu ◽  
Hong Qiao ◽  
Bo Zhang
Keyword(s):  
Supervised Learning ◽  
Learning Model

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

A cost-sensitive semi-supervised learning model based on uncertainty

2017 ◽  
Vol 251 ◽  
pp. 106-114 ◽  
Author(s):  
Hongyu Zhu ◽  
Xizhao Wang
Keyword(s):  
Supervised Learning ◽  
Learning Model ◽  
Model Based

Coverage Estimation of Mobile Network Using Supervised Learning Model on Artificial Estimation Dataset

2021 ◽  
Author(s):  
Feibi Lyu ◽  
Chen Cheng ◽  
Jiajia Zhu ◽  
Xinzhou Cheng ◽  
Lexi Xu ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Mobile Network ◽  
Learning Model ◽  
Coverage Estimation

scholarly journals Prediction of combustion state through a semi-supervised learning model and flame imaging

Fuel ◽  
2020 ◽  
pp. 119745
Author(s):  
Zhezhe Han ◽  
Jian Li ◽  
Biao Zhang ◽  
Md. Moinul Hossain ◽  
Chuanlong Xu
Keyword(s):  
Supervised Learning ◽  
Learning Model ◽  
Flame Imaging
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