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The Adaboost algorithm sequentially chooses some set of features that are effective for classifying training data.
12 The MERSQI framework provides a measure of trial size (single or multiple institutions), validity of assessment instruments used, and the Kirkpatrick level of outcome measures used (a taxonomy for classifying training programmes).
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Normally, the accuracy of each fuzzy rule-based classifier is measured by the number of correctly classified training or testing patterns, while its in-terpretability is measured by the complexity of the model, more specifically, the number of fuzzy rules and the total number of antecedent conditions.
For example, clustering algorithms search for groups of similar records, and classification algorithms find data structures to predict the class label of a previously unseen data record according to annotated (classified) training data.
We classified training and supervision level according to the specifications of the Australian and New Zealand College of Anaesthetists' training programme.
Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8 21) or inv(16 -AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set).
After constructing and classifying the training set, training sequences are first analyzed with their nucleotide composition to calculate the occurrence rate of mono-, di-, and tri-mer nucleotides within a 20-bp window sliding along training sequences.
We need to train a classifier to classify the training patterns correctly.
a profile score cut-off was generated at which level of the metric yielded a 5% false positive rate when classifying the training set.
The accuracy of classifying the training dataset using the constructed decision tree is 73.4%.
The training set was randomly classified for training, validation, and test sets in order to avoid overfitting, and then networks were trained.
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