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The application of various statistical machine learning methods for the identification of bi-heterocyclic drugs that are based on the THz spectra is presented. A comparison of classification efficiency with six algorithms (LDA, QDA, SVM, Naive Bayes, KNN with Euclidean metrics and the cosine similarity) is shown and a complete THz system allowing for the identification of drugs with an efficiency...
We introduce a simple, yet effective, procedure for accurate classification of connected components embedded in biological images. In our method, a training set is generated from user-delineated features of manually-labeled examples; we subsequently train a classifier using the resultant training set. The overall process is described using imaging data acquired from an India-ink perfused C57BL/6J...
This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies a number of desired output (label/response) constraints. LSAT problems naturally arise in many applications in which one is interested in the class of inputs that produce desirable outputs, rather than simply a single optimum...
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