Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in the kernel learning algorithm, so as to take advantage of incomplete information about output values and/or of some prior information about the problem at hand. To this end a generic convex opti...

Ensemble learning aims at combining several slightly different learners to construct stronger learner. Ensemble of a well selected subset of learners would outperform than ensemble of all. However, the well studied accuracy / diversity ensemble pruning framework would lead to over fit of training data, which results a target learner of relatively low generalization ability. We propose to ensemb...

Abstract Stress is a common feeling in people’s day-to-day life, especially at work, being the cause of several health problems and absenteeism. Despite difficulty identifying it properly, studies have established correlation between stress perceivable human features. The problem detecting has attracted significant attention last decade. It been mainly addressed through analysis physiological s...

In this paper, we propose an object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of which are Faster R-CNN, and EfficientDet, in order design a novel robust wheat head model. emphasize on optimizing the performance our proposed final architectures. Furthermore, extensive exploratory data analysis, cleaning, splitting a...

We tackle the problem of semi-automatic road tracking in aerial photos. Our solution to this human-computer interaction problem is to provide a learning approach that integrates naturally input from human experts with automatic tracking of roads. More specifically, our system learns an ensemble of road predictors from human inputs and uses them to further track a road, alerting the human expert...