Classification using high-dimensional features arises frequently in many contemporary
statistical studies such as tumor classification using microarray or other high-throughput …

In statistics and machine learning, classification studies how to automatically learn to make
good qualitative predictions (ie, assign class labels) based on past observations. Examples …

The goal of this paper is to design a statistical test for the camera model identification
problem. The approach is based on the heteroscedastic noise model, which more accurately …

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson
paradigm to deal with asymmetric errors in binary classification with a convex loss ϕ. Given …

The goal of this paper is to propose a statistical model of quantized discrete cosine transform
(DCT) coefficients. It relies on a mathematical framework of studying the image processing …

We formulate a local form of the bipartite ranking problem where the goal is to focus on the
best instances. We propose a methodology based on the construction of real-valued scoring …

The goal of this paper is to design a statistical test for the camera model identification
problem. The approach is based on the generalized noise model that is developed by …

Microwave breast screening has been proposed as a complementary modality to the current
standard of X-ray mammography. In this work, we design three ensemble classification …

This paper investigates the detection of information hidden in digital media by the least
significant bit (LSB) matching scheme. In a theoretical context of known medium parameters …

In this paper, we apply a Bag-of-Features approach to malignant melanoma detection based
on epiluminescence microscopy imaging. Each skin lesion is represented by a histogram of …