Add to ORKGConsiderable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Abstract. Semi-supervised learning has been widely studied in the literature. However, most previous...
Semi-supervised learning algorithms seek to train prediction models on both labelled and unlabelled ...
Considerable progress was recently achieved on semi-supervised learning, which differs from the trad...
One of the most important issues in machine learning is whether one can improve the performance of a...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
In semi-supervised learning, one key strategy in exploiting unlabeled data is trying to estimate its...
We review some of the literature on semi-supervised learning in this paper. Traditional classifiers ...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...
• The main models we have been studying (PAC, mistake-bound) are for supervised learning. – Given la...
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, whil...
Semi-supervised learning aims at training accurate prediction models on labeled and unlabeled data. ...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Abstract. Semi-supervised learning has been widely studied in the literature. However, most previous...
Semi-supervised learning algorithms seek to train prediction models on both labelled and unlabelled ...
Considerable progress was recently achieved on semi-supervised learning, which differs from the trad...
One of the most important issues in machine learning is whether one can improve the performance of a...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
In semi-supervised learning, one key strategy in exploiting unlabeled data is trying to estimate its...
We review some of the literature on semi-supervised learning in this paper. Traditional classifiers ...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...
• The main models we have been studying (PAC, mistake-bound) are for supervised learning. – Given la...
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, whil...
Semi-supervised learning aims at training accurate prediction models on labeled and unlabeled data. ...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Abstract. Semi-supervised learning has been widely studied in the literature. However, most previous...
Semi-supervised learning algorithms seek to train prediction models on both labelled and unlabelled ...