In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

LIBOL is an open-source library for large-scale online learning, which consists of a large
family of efficient and scalable state-of-the-art online learning algorithms for large-scale …

This article proposes a novel online portfolio selection strategy named “Passive Aggressive
Mean Reversion”(PAMR). Unlike traditional trend following approaches, the proposed …

In many prediction tasks, selecting relevant features is essential for achieving good
generalization performance. Most feature selection algorithms consider all features to be a …

This paper introduces the Banditron, a variant of the Perceptron [Rosenblatt, 1958], for the
multiclass bandit setting. The multiclass bandit setting models a wide range of practical …

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 real-world classification tasks, it is difficult to collect training samples from all possible
categories of the environment. Therefore, when an instance of an unseen class appears in …

Industrial control systems are critical to the operation of industrial facilities, especially for
critical infrastructures, such as refineries, power grids, and transportation systems. Similar to …

This review examines kernel methods for online learning, in particular, multiclass
classification. We examine margin-based approaches, stemming from Rosenblatt's original …

In many real-world applications of learning, the environment is open and changes gradually,
which requires the learning system to have the ability of detecting and adapting to the …