Manual examination of retina images for the diagnosis of diabetic retinopathy is a time consuming and error prone process, requiring identification of inconspicuous anomalies like micro-aneurysms and exudates. In this work, we explore machine learning techniques for automatic identification and severity classification of diabetic retinopathy from retina images. The presented approach involves i...

This paper describes a new training approach based on two different techniques (Minimum Classification Error and eigenvoices) in order to achieve a better robustness when only poor training data is provided. In the first two sections of this paper we describe the MCE training and the eigenvoice approach. Then a unified MCE/eigenvoice training algorithm is proposed describing theoretical advanta...

In this paper, a new identity-based identification scheme based on error-correcting codes is proposed. Two well known code-based schemes are combined : the signature scheme by Courtois, Finiasz and Sendrier and an identification scheme by Stern. A proof of security for the scheme in the Random Oracle

We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems. The impulse responses are modeled as Gaussian processes whose autocovariances encode the BIBO stability constraint, as defined by the recently introduced “Stable Spline kernel”. Sparse solutions are obtained by placing exponential hyperpriors on the scale factors of such kernels. Numerical exp...

A system identification and model updating methodology that accounts for factors influencing the reliability of identification is proposed. An important aspect of this methodology is the generation of a population of candidate models. This paper presents an analysis of error sources that are used to define model populations. A case study illustrates the need for such an approach even when a sin...