中文说明：在许多现实世界的应用中，我们处理与多个相关的分类/回归/聚类任务。对于例如，治疗结果的预测（Bickel et al.，2008），预测效果的任务几种药物的组合是相关的。在疾病进展预测中，预测在每个时间点的结果可以被视为一个任务，这些任务是时间相关的（周等。，2011B）。一个简单的方法是独立解决这些任务，忽略了任务关联。在多任务学习，这些相关的任务，同时了解到通过提取和利用适当的共享任务信息。同时学习多个相关任务，有效增加样本大小为每个任务，并提高了预测性能。因此多任务学习是特别有益的当训练样本的大小是小的每一个任务。图1说明了传统单任务学习（STL）和多任务学习（MTL）。在STL，每个任务都是独立的并学会独立。在MTL，多任务学习的同时，利用任务关联
English Description：
In many real-world applications we deal with multiple related classification/regression/clustering tasks. For example, in the prediction of therapy outcome (Bickel et al., 2008), the tasks of predicting the effectiveness of several combinations of drugs are related. In the prediction of disease progression, the prediction of outcome at each time point can be considered as a task and these tasks are temporally related (Zhou et al., 2011b). A simple approach is to solve these tasks independently, ignoring the task relatedness. In multitask learning, these related tasks are learnt simultaneously by extracting and utilizing appropriate shared information across tasks. Learning multiple related tasks simultaneously effectively increases the sample size for each task, and improves the prediction performance. Thus multi-task learning is especially beneficial when the training sample size is small for each task. Figure 1 illustrates the differen