Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Ordinal regression problems are those machine learning problems where the objective is to
classify patterns using a categorical scale which shows a natural order between the labels …

Despite increasing efforts on universal representations for visual recognition, few have
addressed object detection. In this paper, we develop an effective and efficient universal …

As compared to simple actions, activities are much more complex, but semantically
consistent with a human's real life. Techniques for action recognition from sensor generated …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

We address the problem of visual classification with multiple features and/or multiple
instances. Motivated by the recent success of multitask joint covariate selection, we …

Multi-task learning is a learning paradigm which seeks to improve the generalization
performance of a learning task with the help of some other related tasks. In this paper, we …

In this paper, we propose a new framework called domain adaptation machine (DAM) for the
multiple source domain adaption problem. Under this framework, we learn a robust decision …