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are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar wi
Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling
Machine Learning and Artificial Intelligence in Chemical and Biological Sensing
Knowledge Discovery with Support Vector Machines
Neural Networks in Bioprocessing and Chemical Engineering
Machine Learning in Biological Sciences
Chemometrics
Chemometrics
Chemometric Monitoring
Machine Learning in Chemistry
Classification in BioApps
Chemometrics