Automatic classification is one of the basic tasks required in any pattern recognition and
human computer interaction application. In this paper, we discuss training probabilistic …

We describe a new objective for graph-based semi-supervised learning based on
minimizing the Kullback-Leibler divergence between discrete probability measures that …

One of the most important issues in machine learning is whether one can improve the
performance of a supervised learning algorithm by including unlabeled data. Methods that …

An approach to semi-supervised learning is proposed that is based on a Gaussian random
field model. Labeled and unlabeled data are represented as vertices in a weighted graph …

Semi-supervised classifier design that simultaneously utilizes both labeled and unlabeled
samples is a major research issue in machine learning. Existing semisupervised learning …

An example of the influence of unlabeled data in semi-supervised learning. The top panel
shows a decision boundary we might adopt after seeing only one positive (white circle) and …

An application of semi-supervised learning is made to the problem of person identification in
low quality webcam images. Using a set of images of ten people collected over a period of …

We consider semi-supervised learning, learning task from both labeled and unlabeled
instances and in particular, self-training with decision tree learners as base learners. We …

Random Forests (RFs) have become commonplace in many computer vision applications.
Their popularity is mainly driven by their high computational efficiency during both training …

In traditional supervised learning, one uses” labeled” data to build a model. However,
labeling the training data for real-world applications is difficult, expensive, or time …