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Active learning for spectroscopic data regression

https://doi.org/10.1002/cem.2443
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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On multivariate calibration with unlabeled data
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Variable selection in random calibration of near‐infrared instruments: ridge regression and partial least squares regression settings
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Active learning support vector machines for optimal sample selection in classification
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Active Learning for Regression Based on Query by Committee
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The multivariate calibration problem in chemistry solved by the PLS method
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Pool-based active learning in approximate linear regression
inferredcites →https://doi.org/10.1016/j.chemolab.2008.10.001openalex-snapshot
Multiple regression systems for spectrophotometric data analysis
inferredcites →https://doi.org/10.1016/j.chemolab.2008.10.007openalex-snapshot
Support vector machines and its applications in chemistry
inferredcites →https://doi.org/10.1016/j.neunet.2008.06.004openalex-snapshot
A batch ensemble approach to active learning with model selection
inferredcites →https://doi.org/10.1016/s0003-2670(99)00255-xopenalex-snapshot
Analytical control of pharmaceutical production steps by near infrared reflectance spectroscopy
inferredcites →https://doi.org/10.1016/s0169-7439(01)00197-6openalex-snapshot
Some recent trends in the calibration literature
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
inferredcites →https://doi.org/10.1021/ac950964mopenalex-snapshot
On-Line Monitoring of Powder Blend Homogeneity by Near-Infrared Spectroscopy
inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot
A tutorial on support vector regression
inferredcites →https://doi.org/10.1039/a700409eopenalex-snapshot
Determination of Finishing Oils in Acrylic Fibres by Near-infrared Reflectance Spectrometry
inferredcites →https://doi.org/10.1109/72.822506openalex-snapshot
Statistical active learning in multilayer perceptrons
inferredcites →https://doi.org/10.1109/lgrs.2010.2083630openalex-snapshot
Support Vector Machine Active Learning Through Significance Space Construction
inferredcites →https://doi.org/10.1109/titb.2010.2048922openalex-snapshot
Active Learning Methods for Electrocardiographic Signal Classification
inferredcites →https://doi.org/10.1109/tpami.2004.1262340openalex-snapshot
A probabilistic active support vector learning algorithm
inferredcites →https://doi.org/10.1109/tsp.2010.2042491openalex-snapshot
Active Learning and Basis Selection for Kernel-Based Linear Models: A Bayesian Perspective
inferredcites →https://doi.org/10.1366/000370292789619368openalex-snapshot
Potential of Near-Infrared Fourier Transform Raman Spectroscopy in Food Analysis
inferredcites →https://doi.org/10.1613/jair.295openalex-snapshot
Active Learning with Statistical Models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Performance enhancement‐based active learning sample selection method
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Investigating Query by Committee and Inverse‐Distance Based Exploration for Active Learning as Learning Strategies: An Application in Wood Dating
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Active learning strategies with COMBINE analysis: new tricks for an old dog
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One criterion, two merits: A single-criterion-based sample selection method for informativeness and diversity
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Optimizing Empirical and Hybrid Modeling for Advanced Canopy Chlorophyll and Nitrogen Retrieval Technique Using EnMAP Data
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Cluster optimized batch mode active learning sample selection method
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Active learning via collective inference in network regression problems
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National mapping of wetland vegetation leaf area index in China using hybrid model with Sentinel-2 and Landsat-8 data
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Diagnostic and monitoring applications using near infrared (NIR) spectroscopy in cancer and other diseases
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Quantitative analysis of soil nutrition based on FT-NIR spectroscopy integrated with BP neural deep learning
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Adaptive sampling techniques for surrogate modeling to create high-dimension aerodynamic loading response surfaces
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Design and Application of the Sage Surrogate Modeling Software
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A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data
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Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission
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A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions
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Adaptive surrogate models for parametric studies
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Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks
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