Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

Traditional supervised learning makes the closed-world assumption that the classes
appeared in the test data must have appeared in training. This also applies to text learning …

Training and testing are the two phases of a supervised machine learning model. When
these models are trained, validated and tested, it is usually assumed that the test and train …

Existing research on multiclass text classification mostly makes the closed world
assumption, which focuses on designing accurate classifiers under the assumption that all …

Covariate shift occurs prevalently in practice, where the input distributions of the source and
target data are substantially different. Despite its practical importance in various learning …

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned
by the models during training. In this paper, we provide a survey of existing works about …

In classic supervised learning, a learning algorithm takes a fixed training data of several
classes to build a classifier. In this paper, we propose to study a new problem, ie, building a …

In binary classification, there are situations where negative (N) data are too diverse to be
fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios …

Positive-unlabeled learning (PU learning) is an important case of binary classification where
the training data only contains positive and unlabeled samples. The current state-of-the-art …

In traditional graph learning tasks, such as node classification, learning is carried out in a
closed-world setting where the number of classes and their training samples are provided to …