Add to ORKGA graph-based prior is proposed for parametric semi-supervised classi-fication. The prior utilizes both labelled and unlabelled data; it also in-tegrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff be-tween the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is per-formed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.
Semi-supervised learning has attracted a lot of attention because in many data mining applications, ...
Co-training can learn from datasets having a small number of labelled examples and a large number of...
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a...
A graph-based prior is proposed for parametric semi-supervised classi-fication. The prior utilizes b...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
This chapter proposes a simple taxonomy of probabilistic graphical models for the semi-supervised le...
Multiple classifier systems have been originally proposed for supervised classification tasks, and f...
With the increasing data dimensionality, feature selection has become a fundamental task to deal wit...
Semi-Supervised learning methods utilize abundant unlabeled data in order to enlarge the training s...
<p>We apply a graph regularization approach for semi-supervised learning, and the purpose of the pro...
In a traditional machine learning task, the goal is training a classifier using only labeled data (d...
Co-training is a well-known semi-supervised learning technique that applies two basic learners to tr...
Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for...
The paper categorizes and reviews the state-of-the-art approaches to the partially supervised learni...
We review some of the literature on semi-supervised learning in this paper. Traditional classifiers ...
Semi-supervised learning has attracted a lot of attention because in many data mining applications, ...
Co-training can learn from datasets having a small number of labelled examples and a large number of...
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a...
A graph-based prior is proposed for parametric semi-supervised classi-fication. The prior utilizes b...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
This chapter proposes a simple taxonomy of probabilistic graphical models for the semi-supervised le...
Multiple classifier systems have been originally proposed for supervised classification tasks, and f...
With the increasing data dimensionality, feature selection has become a fundamental task to deal wit...
Semi-Supervised learning methods utilize abundant unlabeled data in order to enlarge the training s...
<p>We apply a graph regularization approach for semi-supervised learning, and the purpose of the pro...
In a traditional machine learning task, the goal is training a classifier using only labeled data (d...
Co-training is a well-known semi-supervised learning technique that applies two basic learners to tr...
Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for...
The paper categorizes and reviews the state-of-the-art approaches to the partially supervised learni...
We review some of the literature on semi-supervised learning in this paper. Traditional classifiers ...
Semi-supervised learning has attracted a lot of attention because in many data mining applications, ...
Co-training can learn from datasets having a small number of labelled examples and a large number of...
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a...