Перед вами один из самых интересных учебников по машинному обучению–разделу
искусственного интеллекта, изучающего методы построения моделей, способных …

As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …

We develop multi-class financial misstatement detection models to detect misstatements
with fraud intention. Hennes, Leone and Miller (2008) conducted a post-event analysis of …

Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance.
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …

ROC analysis investigates and employs the relationship between sensitivity and specificity
of a binary classifier. Sensitivity or true positiverate measures the proportion of positives …

Traditional multi-class classification models are based on labeled data and are not
applicable to unlabeled data. To overcome this limitation, this paper presents a multi-class …

Problem definition: Providing fast and reliable delivery services is key to running a
successful online retail business. To achieve a better delivery time guarantee policy, we …

This paper addresses the problem of learning a multiclass classification system that can suit
to any environment. By that we mean that particular (imbalanced) misclassification costs are …

The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are
frequently used in the machine learning community to analyze the performance of binary …

Many classification models, based on support vector machine, have been designed so far to
improve classification performance in both supervised and semi-supervised learning. One of …