Relevance Vector Machine for Content-Based Retrieval of 3D Head Models

2006 ◽  
Author(s):  
Pui Fong Yeung ◽  
Hau San Wong ◽  
Bo Ma ◽  
Horace H.-S. Ip
Keyword(s):  
Relevance Vector Machine ◽  
Content Based Retrieval

scholarly journals Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Kamran Chapi ◽  
Nhat-Duc Hoang ◽  
...  
Keyword(s):  
Spatial Modeling ◽  
Imperialist Competitive Algorithm ◽  
Integrated Approach ◽  
Relevance Vector Machine ◽  
Shallow Landslides ◽  
Competitive Algorithm

The authors wish to make the following corrections to this paper [...]

A comprehensive study on statistical prediction and reduction of tire/road noise

2021 ◽  
pp. 107754632110131
Author(s):  
Somaye Mohammadi ◽  
Abdolreza Ohadi ◽  
Mostafa Irannejad-Parizi
Keyword(s):  
Neural Network ◽  
Road Transport ◽  
Relevance Vector Machine ◽  
Statistical Investigation ◽  
Statistical Prediction ◽  
Support Vector ◽  
Prediction Ability ◽  
Vector Method ◽  
Tire Noise ◽  
Rolling Noise

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.

Developing an image viewer for content-based retrieval and navigation (abstract)

1996 ◽  
Vol 5 (2) ◽  
pp. 15
Author(s):  
Stephen Perry ◽  
Mark Dobie ◽  
Paul Lewis
Keyword(s):  
Content Based Retrieval ◽  
Image Viewer

scholarly journals An Estimation of Ship Collision Risk Based on Relevance Vector Machine

2021 ◽  
Vol 9 (5) ◽  
pp. 538
Author(s):  
Jinwan Park ◽  
Jung-Sik Jeong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Risk Estimation ◽  
Good Method ◽  
Relevance Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Collision Risk ◽  
Estimation Model ◽  
Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

Group Relevance Vector Machine for sparse force localization and reconstruction

2021 ◽  
Vol 161 ◽  
pp. 107900
Author(s):  
Wei Feng ◽  
Qiaofeng Li ◽  
Qiuhai Lu ◽  
Chen Li ◽  
Bo Wang
Keyword(s):  
Relevance Vector Machine
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