Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency suppor...

One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for deening subgroups of r, and a selection predicate, nd all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for nding all such descriptions. We give bounds for the number of database accesses that the algorithm...

This paper formalizes a latent variable inference problem we call supervised pattern discovery, the goal of which is to find sets of observations that belong to a single “pattern.” We discuss two versions of the problem and prove uniform risk bounds for both. In the first version, collections of patterns can be generated in an arbitrary manner and the data consist of multiple labeled collection...

This paper formalizes a latent variable inference problem we call supervised pattern discovery, the goal of which is to find sets of observations that belong to a single “pattern.” We discuss two versions of the problem and prove uniform risk bounds for both. In the first version, collections of patterns can be generated in an arbitrary manner and the data consist of multiple labeled collection...

Many tasks of contemporary Molecular Biology rely increasingly on automated techniques for the discovery of interesting patterns and associations among them, both in individual sequences and across sequence families. A number of computational models and tools have been set up in recent years in response to these needs. This paper concentrates on approaches based on discrete combinatorial algori...

In this paper we present the budgeted biomarker discovery problem as an alternative to the association studies traditionally used to identify biomarkers. We present several strong arguments to show why adopting this new problem will help solve issues in reproducibilty and understanding of association studies. Additionaly, we present several algorithms for this problem and show their performance...

Abstract: The false discovery rate (FDR) and false nondiscovery rate (FNDR) have received considerable attention in the literature on multiple testing. These performance measures are also appropriate for classification, and in this work we develop generalization error analyses for FDR and FNDR when learning a classifier from labeled training data. Unlike more conventional classification perform...

This paper studies the problem of identifying erroneous/correct sentences. The problem has important applications, e.g., providing feedback for writers of English as a Second Language, controlling the quality of parallel bilingual sentences mined from the Web, and evaluating machine translation results. In this paper, we propose a new approach to detecting erroneous sentences by integrating pat...