Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …

With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

近年来, 迁移学习已经引起了广泛的关注和研究. 迁移学习是运用已存有的知识对不同但相关
领域问题进行求解的一种新的机器学习方法. 它放宽了传统机器学习中的两个基本假设:(1) …

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 …

A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …

Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL)
models have been proposed and shown promising performance. However, because of very …

This paper presents new and effective algorithms for learning kernels. In particular, as
shown by our empirical results, these algorithms consistently outperform the so-called …

We present a method for learning a low-dimensional representation which is shared across
a set of multiple related tasks. The method builds upon the wellknown 1-norm regularization …

We present a method for learning sparse representations shared across multiple tasks. This
method is a generalization of the well-known single-task 1-norm regularization. It is based …

The use of kernel functions is a common technique to extract important features from
datasets. A quantum computer can be used to estimate kernel entries as transition …