In many information processing tasks, labels are usually expensive and the unlabeled data
points are abundant. To reduce the cost on collecting labels, it is crucial to predict which …

Semi-supervised learning concerns the problem of learning in the presence of labeled and
unlabeled data. Several boosting algorithms have been extended to semi-supervised …

We analyze the multi-view regression problem where we have two views X=(X (1), X (2)) of
the input data and a target variable Y of interest. We provide sufficient conditions under …

Abstract Recently, restricted Boltzmann machines (RBMs) have attracted considerable
interest in machine learning field due to their strong ability to extract features. Given some …

In many real-world learning scenarios, acquiring a large amount of labeled training data is
expensive and time-consuming. Semi-supervised learning (SSL) is the machine learning …

Boosting is a very successful classification algorithm that produces a linear combination of"
weak" classifiers (aka base learners) to obtain high quality classification models. In this …

Water is a scarce resource and must be efficiently managed. One of the purposes of efficient
management should be reducing water losses and increasing supply performance in the …

Boosting algorithms, especially AdaBoost, have attracted great attention in computer vision.
In the early version of boosting algorithms, the weak classifier selection and the strong …

Labeling image collections is a tedious task, especially when multiple labels have to be
chosen for each image. In this paper we introduce a new framework that extends state of the …

In supervised learning tasks, boosting can combine multiple weak learners into a stronger
one. AdaBoost is one of the most popular boosting algorithms, which is widely used and …