Add to ORKGIn this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training pro-cess is viewed as a combinative label propaga-tion over two views; this provides a possibility to bring the graph-based and disagreement-based semi-supervised methods into a unified framework. With the analysis we get some insight that has not been disclosed by pre-vious theoretical studies. In particular, we provide the sufficient and necessary condi-tion for co-training to succeed. We also dis-cuss the relationship to previous theoretical results and give some other interesting impli-cations of our results, such as combination of weight matrices and view split. 1
Co-training can learn from datasets having a small number of labelled examples and a large number of...
The traditional setting of supervised learning requires a large amount of labeled training examples ...
Co-training is a well-known semi-supervised learning technique that applies two basic learners to tr...
Abstract—Co-training, a paradigm of semi-supervised learning, is promised to alleviate effectively t...
Co-training is a famous semi-supervised learning paradigm exploiting unlabeled data with two views. ...
Co-training is a famous semi-supervised learning paradigm exploiting unlabeled data with two views. ...
Abstract. Co-training, a paradigm of semi-supervised learning, may alleviate effectively the data sc...
Recently, Semi-Supervised learning algorithms such as co-training are used in many domains. In co-tr...
Semi-supervised learning has attracted much attention over the past decade because it provides the a...
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as ...
Co-training is a semi supervised learning method that effectively learns from a pool of labeled and ...
Abstract—Co-training is one of the major semi-supervised learning paradigms which iteratively trains...
Co-training is a well known semi-supervised learning algorithm, in which two classifiers are trained...
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as ...
Facing the problem of massive unlabeled data and limited labeled samples, semi-supervised learning i...
Co-training can learn from datasets having a small number of labelled examples and a large number of...
The traditional setting of supervised learning requires a large amount of labeled training examples ...
Co-training is a well-known semi-supervised learning technique that applies two basic learners to tr...
Abstract—Co-training, a paradigm of semi-supervised learning, is promised to alleviate effectively t...
Co-training is a famous semi-supervised learning paradigm exploiting unlabeled data with two views. ...
Co-training is a famous semi-supervised learning paradigm exploiting unlabeled data with two views. ...
Abstract. Co-training, a paradigm of semi-supervised learning, may alleviate effectively the data sc...
Recently, Semi-Supervised learning algorithms such as co-training are used in many domains. In co-tr...
Semi-supervised learning has attracted much attention over the past decade because it provides the a...
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as ...
Co-training is a semi supervised learning method that effectively learns from a pool of labeled and ...
Abstract—Co-training is one of the major semi-supervised learning paradigms which iteratively trains...
Co-training is a well known semi-supervised learning algorithm, in which two classifiers are trained...
Co-training is a method for combining labeled and unlabeled data when examples can be thought of as ...
Facing the problem of massive unlabeled data and limited labeled samples, semi-supervised learning i...
Co-training can learn from datasets having a small number of labelled examples and a large number of...
The traditional setting of supervised learning requires a large amount of labeled training examples ...
Co-training is a well-known semi-supervised learning technique that applies two basic learners to tr...