## Semi-Supervised Multitask Learning (2007)

Citations: | 23 - 5 self |

### BibTeX

@MISC{Liu07semi-supervisedmultitask,

author = {Qiuhua Liu and Xuejun Liao and Hui Li and Jason Stack and Lawrence Carin},

title = {Semi-Supervised Multitask Learning },

year = {2007}

}

### OpenURL

### Abstract

Context plays an important role when performing classification, and in this paper we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks (multiple distinct data collections). This is referred to as multi-task learning (MTL), and is implemented here in a statistical manner, using a simplified form of the Dirichlet process. In addition, when performing many classification tasks one has simultaneous access to all unlabeled data that must be classified, and therefore there is an opportunity to place the classification of any one feature vector within the context of all unlabeled feature vectors; this is referred to as semi-supervised learning. In this paper we integrate MTL and semi-supervised learning into a single framework, thereby exploiting two forms of contextual information. Results are presented on a “toy” example, to demonstrate the concept, and the algorithm is also applied to three real data sets.

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Citation Context ... much recent work on improving the generalization of classifiers based on using information sources beyond the labeled data. These studies fall into two major categories: (i) semi-supervised learning =-=[8, 11, 14, 9]-=- and (ii) multitask learning (MTL) [3, 1, 12]. The former employs the information from the data manifold, in which the manifold information provided by the usually abundant unlabeled data is exploited... |

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