Abstract We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types systems: prototype-based vector quantization (LVQ) classification and shallow, layered neural networks regression tasks. investigate so-called student–teacher scenarios which systems are trained from stream high-dimensional, labeled data...

Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information. The enables contrast with another view of single image but enlarges training time memory usage. To exploit the strength multi-views while avoiding high computation cost, we introduce multi-exit architecture outputs multip...

MOTIVATION Protein-protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pai...

Weakly supervised object detection has recently received much attention, since it only requires imagelevel labels instead of the bounding-box labels consumed in strongly supervised learning. Nevertheless, the save in labeling expense is usually at the cost of model accuracy. In this paper, we propose a simple but effective weakly supervised collaborative learning framework to resolve this probl...

The development of robust classification model is among the important issues in computer vision. This paper deals with weakly supervised learning that generalizes the supervised and semi-supervised learning. In weakly supervised learning training data are given as the priors of each class for each sample. We first propose a weakly supervised strategy for learning soft decision trees. Besides, t...

In this paper, a semi-supervised modeling framework that combines feature-based (x) data and graph-based (G) data for classification/regression of the response Y is presented. In this semi-supervised setting, Y is observed for a subset of the observations (labeled) and missing for the remainder (unlabeled). The Propagated Scoring algorithm proposed for fitting this model is a semi-supervised fi...

Feature selection is fundamental in many data mining or machine learning applications. Most of the algorithms proposed for this task make the assumption that the data are either supervised or unsupervised, while in practice supervised and unsupervised samples are often simultaneously available. Semi-supervised feature selection is thus needed, and has been studied quite intensively these past f...

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 use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don’t have a complete understanding of their effective...