Online passive aggressive active learning and its applications

Abstract We investigate online active learning techniques for classification tasks in data stream mining applications. Unlike traditional learning approaches (either batch or online learning) that often require to request the class label of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, which aims to maximize classification performance using minimal human labeling effort during the entire online stream data mining task. In this paper, we present a new family of algorithms for online active learning called Passive-Aggressive Active (PAA) learning algorithms by adapting the popular Passive-Aggressive algorithms in an online active learning setting. Unlike the conventional Perceptron-based approach that employs only the misclassified instances for updating the model, the proposed PAA learning algorithms not only use the misclassified instances to update the classifier, but also exploit correctly classified examples with low prediction confidence. We theoretically analyse the mistake bounds of the proposed algorithms and conduct extensive experiments to examine their empirical performance, in which encouraging results show clear advantages of our algorithms over the baselines

Online Passive Aggressive Active Learning and its Applications

Best Runner-Up Paper Award, 26-28 November 2014</p

The therapeutic use of videogames within secure forensic settings: a review of the literature and application to practice

Engagement in leisure pursuits that involves the use of tools and objects and the exploration of a new environment can provide a success experience that leads to increased feelings of competence and mastery. Such experiences are considered important in the rehabilitation of forensic clients. The findings from videogame research within a general population are compared with those among mental health and forensic clients. Within the general population, videogames may provide opportunities for social interaction and the expression of creativity and humour as well as offering a graded approach to building computer skills. Within a forensic population, videogames have been found to be a normalising, age-appropriate and culturally appropriate activity, useful in engaging clients and improving self-concept and locus of control. The findings suggest that videogame play offers access to a safe virtual environment that encourages exploration and mastery and that it may be a useful therapeutic tool in secure settings where such opportunities are often limited. The use and potential contraindications of videogames within a forensic setting, the content of certain games and their possible influence on behaviour and the implications for future research are also discussed

Online Importance Weight Aware Updates

An importance weight quantifies the relative importance of one example over another, coming up in applications of boosting, asymmetric classification costs, reductions, and active learning. The standard approach for dealing with importance weights in gradient descent is via multiplication of the gradient. We first demonstrate the problems of this approach when importance weights are large, and argue in favor of more sophisticated ways for dealing with them. We then develop an approach which enjoys an invariance property: that updating twice with importance weight $h$ is equivalent to updating once with importance weight $2h$. For many important losses this has a closed form update which satisfies standard regret guarantees when all examples have $h=1$. We also briefly discuss two other reasonable approaches for handling large importance weights. Empirically, these approaches yield substantially superior prediction with similar computational performance while reducing the sensitivity of the algorithm to the exact setting of the learning rate. We apply these to online active learning yielding an extraordinarily fast active learning algorithm that works even in the presence of adversarial noise