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The standard approach to rank the performance of several classifiers for a given classification problem is via an independent labeled validation dataset. However, in various applications only unlabele...
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disabling disease. Altho...
Data mining involves the systematic analysis of large data sets , and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of...
Structured prediction tasks pose a fundamental trade-o between the need for model com-plexity to increase predictive power and the limited computational resources for inference in the exponentially-s...
This paper addresses the prediction of functional time series. Existing contributions to this problem have largely focused on the special case of rst-order functional autoregressive processes because...
Computer codes are widely used to describe physical processes in lieu of physical observations.In some cases, more than one computer simulator, each with di erent degrees of delity, can be used to ex...
The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (pnparadigm).A PAC-Bayesian strategy is investigated, delivering oracle inequaliti...
We analyze the results of the German Team Handball Bundesliga for ten seasons in a model-free statistical time series approach. We will show that the home advantage is nearly negligible compared to th...
This paper presents the application of a particle filter for data assimilation in the context of puff-based dispersion models. Particle filters provide estimates of the higher moments, and are well su...
We study the problem of sequential prediction of categorical data and discuss a generalisation of Blackwell's algorithm on 0-1 data. The arguments are based on Blackwell's approachability results give...
We investigate deterministic design matrices for the fundamental problems of error prediction and model selection. Our deterministic design matrices are constructed from unbalanced expander graphs, a...
The problem of prediction in functional linear regression is conventionally addressed by reducing dimension via the standard principal component basis. In this paper we show that weighted least-square...

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