Add to ORKGTraditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in any supervised approach such as co-training or self-learning. We empirically show that classification performance increases by improving the semi-supervised algorithm’s ability to correctly assign labels to previously-unlabelled data. 1
We present an algorithm for multiclass semi-supervised learning, which is learning from a limited am...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Self-labeled techniques are semi-supervised classification methods that address the shortage of labe...
Traditional supervised classification algorithms require a large number of labelled examples to perf...
We review some of the literature on semi-supervised learning in this paper. Traditional classifiers ...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelle...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
Domains like text classification can easily supply large amounts of unlabeled data, but labeling its...
In the general framework of semi-supervised learning from labeled and unlabeled data, we consider ...
International audienceDomains like text classification can easily supply large amounts of unlabeled ...
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between...
Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...
This thesis focuses on how unlabeled data can improve supervised learning classi-fiers in all contex...
We present an algorithm for multiclass semi-supervised learning, which is learning from a limited am...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Self-labeled techniques are semi-supervised classification methods that address the shortage of labe...
Traditional supervised classification algorithms require a large number of labelled examples to perf...
We review some of the literature on semi-supervised learning in this paper. Traditional classifiers ...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelle...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
Domains like text classification can easily supply large amounts of unlabeled data, but labeling its...
In the general framework of semi-supervised learning from labeled and unlabeled data, we consider ...
International audienceDomains like text classification can easily supply large amounts of unlabeled ...
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between...
Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...
This thesis focuses on how unlabeled data can improve supervised learning classi-fiers in all contex...
We present an algorithm for multiclass semi-supervised learning, which is learning from a limited am...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Self-labeled techniques are semi-supervised classification methods that address the shortage of labe...