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Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier
Whether based on models, training data or a combination, classifiers place (possibly complex) input data into one of a relatively small number of output categories.
Application of Markov Structure of Genomes to Outlier Identification and Read Classification
In this paper we apply the structure of genomes as second-order Markov processes specified by the distributions of successive triplets of bases to two bioinformatics problems: identification of outliers in genome databases and read classification in metagenomics, using real coronavirus and adenovirus data.
Measuring Quality of DNA Sequence Data via Degradation
We propose and apply a novel paradigm for characterization of genome data quality, which quantifies the effects of intentional degradation of quality.
Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly
Specified Certainty Classification (SCC) is a new paradigm for employing classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities.
