In many real-world tasks, there are abundant unlabeled examples but the number of labeled
training examples is limited, because labeling the examples requires human efforts and …

摘要传统监督学习通常需使用大量有标记的数据样本作为训练例, 而在很多现实问题中,
人们虽能容易地获得大批数据样本, 但为数据提供标记却需耗费很多人力物力. 那么 …

Semi-supervised learning and ensemble learning are two important machine learning
paradigms. The former attempts to achieve strong generalization by exploiting unlabeled …

Co-training is one of the major semi-supervised learning paradigms that iteratively trains two
classifiers on two different views, and uses the predictions of either classifier on the …

The Discovery Challenge 2006 deals with personalized spam filtering and generalization
across related learning tasks. In this overview of the challenge we motivate and describe the …

Most spam filters include some automatic pattern classifiers based on machine learning and
pattern recognition techniques. Such classifiers often require a large training set of labeled …

Tri-training method proposed by Zhou et al., is an excellent method for semi-supervised
classification; nevertheless, the heavy computational burden caused by the retraining …

This paper proposes a semi-supervised Bayesian ARTMAP (SSBA) which integrates the
advantages of both Bayesian ARTMAP (BA) and Expectation Maximization (EM) algorithm …

摘要Co-training 是一种主流的半监督学习算法. 该算法中两视图下的分类器通过迭代的方式,
互为对方从无标记样本集中挑选新增样本, 以更新对方训练集. Co-training 以分类器的后验概率 …

We consider the problem of multi-modal pattern recognition under the assumption that a
kernel-based approach is applicable within each particular modality. The Cartesian product …