On Weighted Support Vector Regression

Han, Xixuan; Clemmensen, Line Katrine Harder

doi:10.1002/qre.1654

Cited by 24 publications

(8 citation statements)

References 24 publications

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“…At any rate, reducing the effect of noise in the training data by emphasizing the most reliable observations could enhance predictive ability. For instance, with weighted SVR it is possible to consider different contributions for each data point to the learning function by introducing a weighting vector in the risk function to be minimized (Han & Clemmensen, 2014), whereas for RF, tree‐level weights can be incorporated to strengthen the contribution of the more accurate trees in the final ensembled prediction (Winham et al . 2013).…”

Section: Resultsmentioning

confidence: 99%

Genome‐enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods

et al. 2020

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SummaryThis study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregnancy (EP) and stayability (STAY). The numbers of genotyped animals and SNP markers available were 2342 and 321 419 (AFC), 4671 and 309 486 (SC), 2681 and 319 619 (STAY) and 3356 and 319 108 (EP). Predictive ability of support vector regression (SVR), Bayesian regularized artificial neural network (BRANN) and random forest (RF) were compared with results obtained using parametric models (genomic best linear unbiased predictor, GBLUP, and Bayesian least absolute shrinkage and selection operator, BLASSO). A 5‐fold cross‐validation strategy was performed and the average prediction accuracy (ACC) and mean squared errors (MSE) were computed. The ACC was defined as the linear correlation between predicted and observed breeding values for categorical traits (EP and STAY) and as the correlation between predicted and observed adjusted phenotypes divided by the square root of the estimated heritability for continuous traits (AFC and SC). The average ACC varied from low to moderate depending on the trait and model under consideration, ranging between 0.56 and 0.63 (AFC), 0.27 and 0.36 (SC), 0.57 and 0.67 (EP), and 0.52 and 0.62 (STAY). SVR provided slightly better accuracies than the parametric models for all traits, increasing the prediction accuracy for AFC to around 6.3 and 4.8% compared with GBLUP and BLASSO respectively. Likewise, there was an increase of 8.3% for SC, 4.5% for EP and 4.8% for STAY, comparing SVR with both GBLUP and BLASSO. In contrast, the RF and BRANN did not present competitive predictive ability compared with the parametric models. The results indicate that SVR is a suitable method for genome‐enabled prediction of reproductive traits in Nellore cattle. Further, the optimal kernel bandwidth parameter in the SVR model was trait‐dependent, thus, a fine‐tuning for this hyper‐parameter in the training phase is crucial.

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Section: Resultsmentioning

confidence: 99%

Genome‐enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods

et al. 2020

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“…To address this relative importance in the trajectory estimation, increased weight is given to the last samples. In the case of SVR, the sample weighting re-scales the C parameter, which means that the model puts more emphasis on getting these points right [37]. To compare the experimental results, a three-degree polynomial regression is also implemented.…”

Section: B Resultsmentioning

confidence: 99%

An Architecture for Person-Following using Active Target Search

Kim¹,

Arduengo²,

Walker³

et al. 2018

Preprint

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This paper addresses a novel architecture for person-following robots using active search. The proposed system can be applied in real-time to general mobile robots for learning features of a human, detecting and tracking, and finally navigating towards that person. To succeed at personfollowing, perception, planning, and robot behavior need to be integrated properly. Toward this end, an active target searching capability, including prediction and navigation toward vantage locations for finding human targets, is proposed. The proposed capability aims at improving the robustness and efficiency for tracking and following people under dynamic conditions such as crowded environments. A multi-modal sensor information approach including fusing an RGB-D sensor and a laser scanner, is pursued to robustly track and identify human targets. Bayesian filtering for keeping track of human and a regression algorithm to predict the trajectory of people are investigated. In order to make the robot autonomous, the proposed framework relies on a behavior-tree structure. Using Toyota Human Support Robot (HSR), real-time experiments demonstrate that the proposed architecture can generate fast, efficient person-following behaviors.

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“…In fact, some models, for instance support vector machine (SVM) models, have strong prediction capability partly because of its inherent feature extraction procedures in themselves. SVM increases the dimension of the feature space by mapping the original features into a new feature space (kernel-induced space), and regularizes a Lasso model in the kernel-induced space and finally decreases the effective dimension of the solution [3].…”

Section: Introductionmentioning

confidence: 99%