scholarly journals Analyzing relevance vector machines using a single penalty approach

2021 ◽  
Author(s):  
Anand Dixit ◽  
Vivekananda Roy
Keyword(s):  
Penalty Approach ◽  
Relevance Vector Machines ◽  
Vector Machines

Relevance vector machines using weighted expected squared distance for ore grade estimation with incomplete data

2016 ◽  
Vol 8 (5) ◽  
pp. 1655-1666 ◽  
Author(s):  
Yukui Zhang ◽  
Shiji Song ◽  
Keyou You ◽  
Xunan Zhang ◽  
Cheng Wu
Keyword(s):  
Incomplete Data ◽  
Grade Estimation ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Ore Grade

scholarly journals The Mixture of Multi-kernel Relevance Vector Machines Model

2012 ◽  
Author(s):  
Konstantinos Blekas ◽  
Aristidis Likas
Keyword(s):  
Relevance Vector Machines ◽  
Vector Machines

scholarly journals Developing a forecasting model of concrete compressive strength using relevance vector machines

2014 ◽  
Vol 3 (2) ◽  
pp. 224 ◽  
Author(s):  
Mohammad Awwad
Keyword(s):  
Compressive Strength ◽  
Early Stage ◽  
Mixing Time ◽  
Relevance Vector Machine ◽  
Strength Increase ◽  
Concrete Compressive Strength ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Strength Material ◽  
Cement Mixture

We analyze results of two experiments that tested effect of adding Silica on the compressive strength of concrete at early stage and after long period. The two experiments evaluated different silica/cement ratios for different mixing periods. Adding Silica to concrete mix produce high early strength material which is highly desirable in airports and highways. More than 90 samples of different silica/cement ratios are tested for compressive strength at 3 and 28 days. Test results showed high early up to 60 MPa. Strength increase is proportional with the increase of silica/cement ratio and mixing time with maximum at ratio of 15/100 and 30 minutes mixing time. A relevance Vector Machine (RVM) model is developed to predict concrete compressive strength using concrete mixture inputs information. RVM model predictions matched experimental data closely. The developed model can be used to predict compressive strength in future periods based on initial information related to cement mixture. Keywords: Relevance Vector Machine, Silicate Percent, Prediction Model, Milling Time, Compressive Strength, Concrete.

scholarly journals DRACP: a novel method for identification of anticancer peptides

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Amino Acids ◽  
Chemical Characteristics ◽  
Surgical Operation ◽  
Anticancer Peptides ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Amino Acids Composition ◽  
Novel Method ◽  
Hydrophilic Residues ◽  
Experimental Technology

Abstract Background Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs. Results Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets. Conclusion We developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

Relevance vector machines for multivariate calibration purposes

2008 ◽  
Vol 22 (11-12) ◽  
pp. 686-694 ◽  
Author(s):  
Noslen Hernández ◽  
Isneri Talavera ◽  
Angel Dago ◽  
Rolando J. Biscay ◽  
Marcia M. Castro Ferreira ◽  
...  
Keyword(s):  
Multivariate Calibration ◽  
Relevance Vector Machines ◽  
Vector Machines

Relevance vector machines for sparse learning of biophysical parameters

2005 ◽  
Author(s):  
G. Camps-Valls ◽  
L. Gomez-Chova ◽  
J. Vila-Francés ◽  
J. Amorós-López ◽  
J. Muñoz-Mar ◽  
...  
Keyword(s):  
Sparse Learning ◽  
Biophysical Parameters ◽  
Relevance Vector Machines ◽  
Vector Machines

scholarly journals Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine

2019 ◽  
Vol 9 (10) ◽  
pp. 2064 ◽  
Author(s):  
Yang Liu ◽  
Yicheng Ye ◽  
Qihu Wang ◽  
Xiaoyun Liu ◽  
Weiqi Wang
Keyword(s):  
Relevance Vector Machine ◽  
Surrounding Rock ◽  
Prediction Errors ◽  
Test Results ◽  
Factors Affecting ◽  
Relevance Vector Machines ◽  
Five Factors ◽  
Vector Machines ◽  
Practical Engineering ◽  
The Difference

By applying the Wavelet Relevance Vector Machine (WRVM) method, this research proposes the loose zone of roadway surrounding rock prediction. Based on the theory of relevance vector machine (RVM), the wavelet function is introduced to replace the original Gauss function as the model kernel function to form the WRVM. Five factors affecting the loose zone of roadway surrounding rock are selected as the model input, and the prediction model of the loose zone of roadway surrounding rock based on WRVM is established. By using cross-validation method, the kernel parameters of three kinds of wavelet relevance vector machines (RVMs) are calculated. By comparing and analyzing the root mean square (RMS) error of the test results of each predictive model, the advantages and accuracy of the model are verified. In practical engineering applications, the average relative prediction errors of the Mexican relevance vector machine, the Morlet relevance vector machine and the difference of Gaussian (DOG) relevance vector machine models are accordingly 4.581%, 4.586% and 4.575%. The square correlation coefficient of the predicted samples is 0.95 > 0.9, which further verifies the accuracy and reliability of the proposed method.

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