Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …

Regression problems are pervasive in real-world applications. Generally a substantial
amount of labeled samples are needed to build a regression model with good …

It is well known that active learning can simultaneously improve the quality of the
classification model and decrease the complexity of training instances. However, several …

Active learning (AL) is a machine-learning approach for reducing the data labeling effort.
Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a …

Active learning is well-motivated in many supervised learning tasks where unlabeled data
may be abundant but labeled examples are expensive to obtain. The goal of active learning …

We propose a new statistical approach to the problem of inlier-based outlier detection, ie,
finding outliers in the test set based on the training set consisting only of inliers. Our key idea …

In this paper, we propose three criteria for efficient sample selection in case of data stream
regression problems within an online active learning context. The selection becomes …

This paper proposes an active learning (AL) algorithm to solve regression problems based
on inverse-distance weighting functions for selecting the feature vectors to query. The …

Methods for directly estimating the ratio of two probability density functions have been
actively explored recently since they can be used for various data processing tasks such as …

This chapter provides an all-round picture of the development and advances in the fields of
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …