Matches in SemOpenAlex for { <https://semopenalex.org/work/W3014817382> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W3014817382 endingPage "104014" @default.
- W3014817382 startingPage "104014" @default.
- W3014817382 abstract "In Near-infrared (NIR) spectroscopy qualitative analysis, noise caused data quality problem has been a bottleneck to further enhance the prediction accuracy. Appropriate preprocessing methods can reduce the influence of noise; and robust models have higher tolerance for noise disturbance. However, these methods treat all the wavelengths equally. In fact, the spectra at different wavelengths may have highly different level of noise. This paper presents a new noise-level-penalizing robust Gaussian process (NLP-RGP) regression for NIR spectroscopy quantitative analysis. The novel noise level penalizing mechanism penalize the spectra features according to their noise level, i.e., encourage the model to prefer the less noisy features over high noisy features. Gaussian process (GP) is a nonparametric machine learning method based on kernel and Bayesian inference framework; with a noise model of heavy-tailed distribution, robust Gaussian process can handle the abnormal sample data better. Experiments were taken on the determination of the total soluble solids content of navel oranges based on their surface NIR spectra. The NLP-GP outperforms the robust Gaussian process model and least squares support vector machines (LS-SVM), the state of art method. Moreover, the NLP-RGP performs even better than the NLP-GP, achieving the best prediction accuracy among all the models. This demonstrates the effectiveness of noise level penalizing mechanism, and the noise level penalizing mechanism and robust mechanism of Gaussian process can be integrated together well." @default.
- W3014817382 created "2020-04-10" @default.
- W3014817382 creator A5001945317 @default.
- W3014817382 creator A5005912103 @default.
- W3014817382 creator A5027960395 @default.
- W3014817382 creator A5029827598 @default.
- W3014817382 creator A5043621578 @default.
- W3014817382 date "2020-06-01" @default.
- W3014817382 modified "2023-10-17" @default.
- W3014817382 title "Noise level penalizing robust Gaussian process regression for NIR spectroscopy quantitative analysis" @default.
- W3014817382 cites W1249843181 @default.
- W3014817382 cites W2007808016 @default.
- W3014817382 cites W2038096116 @default.
- W3014817382 cites W2040975718 @default.
- W3014817382 cites W2047646095 @default.
- W3014817382 cites W2099461103 @default.
- W3014817382 cites W2148060539 @default.
- W3014817382 cites W2344934955 @default.
- W3014817382 cites W2523799825 @default.
- W3014817382 cites W2769697561 @default.
- W3014817382 cites W2771222472 @default.
- W3014817382 cites W2775261694 @default.
- W3014817382 cites W2793639281 @default.
- W3014817382 cites W2795031936 @default.
- W3014817382 cites W2901239330 @default.
- W3014817382 cites W2910523389 @default.
- W3014817382 cites W2954831495 @default.
- W3014817382 cites W2960991491 @default.
- W3014817382 cites W865398354 @default.
- W3014817382 cites W911782989 @default.
- W3014817382 doi "https://doi.org/10.1016/j.chemolab.2020.104014" @default.
- W3014817382 hasPublicationYear "2020" @default.
- W3014817382 type Work @default.
- W3014817382 sameAs 3014817382 @default.
- W3014817382 citedByCount "9" @default.
- W3014817382 countsByYear W30148173822021 @default.
- W3014817382 countsByYear W30148173822022 @default.
- W3014817382 countsByYear W30148173822023 @default.
- W3014817382 crossrefType "journal-article" @default.
- W3014817382 hasAuthorship W3014817382A5001945317 @default.
- W3014817382 hasAuthorship W3014817382A5005912103 @default.
- W3014817382 hasAuthorship W3014817382A5027960395 @default.
- W3014817382 hasAuthorship W3014817382A5029827598 @default.
- W3014817382 hasAuthorship W3014817382A5043621578 @default.
- W3014817382 hasConcept C11413529 @default.
- W3014817382 hasConcept C115961682 @default.
- W3014817382 hasConcept C119857082 @default.
- W3014817382 hasConcept C12267149 @default.
- W3014817382 hasConcept C147597530 @default.
- W3014817382 hasConcept C153180895 @default.
- W3014817382 hasConcept C154945302 @default.
- W3014817382 hasConcept C163716315 @default.
- W3014817382 hasConcept C185592680 @default.
- W3014817382 hasConcept C41008148 @default.
- W3014817382 hasConcept C4199805 @default.
- W3014817382 hasConcept C61326573 @default.
- W3014817382 hasConcept C99498987 @default.
- W3014817382 hasConceptScore W3014817382C11413529 @default.
- W3014817382 hasConceptScore W3014817382C115961682 @default.
- W3014817382 hasConceptScore W3014817382C119857082 @default.
- W3014817382 hasConceptScore W3014817382C12267149 @default.
- W3014817382 hasConceptScore W3014817382C147597530 @default.
- W3014817382 hasConceptScore W3014817382C153180895 @default.
- W3014817382 hasConceptScore W3014817382C154945302 @default.
- W3014817382 hasConceptScore W3014817382C163716315 @default.
- W3014817382 hasConceptScore W3014817382C185592680 @default.
- W3014817382 hasConceptScore W3014817382C41008148 @default.
- W3014817382 hasConceptScore W3014817382C4199805 @default.
- W3014817382 hasConceptScore W3014817382C61326573 @default.
- W3014817382 hasConceptScore W3014817382C99498987 @default.
- W3014817382 hasLocation W30148173821 @default.
- W3014817382 hasOpenAccess W3014817382 @default.
- W3014817382 hasPrimaryLocation W30148173821 @default.
- W3014817382 hasRelatedWork W2041399278 @default.
- W3014817382 hasRelatedWork W2056016498 @default.
- W3014817382 hasRelatedWork W2136184105 @default.
- W3014817382 hasRelatedWork W2160451891 @default.
- W3014817382 hasRelatedWork W2336974148 @default.
- W3014817382 hasRelatedWork W2389470892 @default.
- W3014817382 hasRelatedWork W3013515612 @default.
- W3014817382 hasRelatedWork W3195168932 @default.
- W3014817382 hasRelatedWork W2187500075 @default.
- W3014817382 hasRelatedWork W2345184372 @default.
- W3014817382 hasVolume "201" @default.
- W3014817382 isParatext "false" @default.
- W3014817382 isRetracted "false" @default.
- W3014817382 magId "3014817382" @default.
- W3014817382 workType "article" @default.