Sourour Ammar

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To explore the Perturb and Combine idea for estimating probability densities, we study mixtures of tree structured Markov networks derived by bagging combined with the Chow and Liu maximum weight spanning tree algorithm, or by pure random sampling. We empirically assess the performances of these methods in terms of accuracy, with respect to mixture models(More)
The present work analyzes different randomized methods to learn Markov tree mixtures for density estimation in very high-dimensional discrete spaces (very large number n of discrete variables) when the sample size (N) is very small compared to n. Several subquadratic relaxations of the Chow-Liu algorithm are proposed, weakening its search procedure. We(More)
In this work we explore the Perturb and Combine idea, celebrated in supervised learning, in the context of probability density estimation in high-dimensional spaces with graphical probabilistic models. We propose a new family of unsupervised learning methods of mixtures of large ensembles of randomly generated tree or poly-tree structures. The specific(More)
The transformation of Euler-Lagrange systems, with the variable of position as output, in order to solve some interesting problem as the design of observer is considered in this paper. First, we will provide a necessary and sufficient condition, which ensures the transformation of such system into some structure affine in the velocities, as well as a method(More)
We consider algorithms for generating Mixtures of Bagged Markov Trees, for density estimation. In problems defined over many variables and when few observations are available, those mixtures generally outperform a single Markov tree maximizing the data likelihood, but are far more expensive to compute. In this paper, we describe new algorithms for(More)
This talk will survey some of the possible roles that machine learning researchers can play in informing and improving clinical practice. Clinical decision making, particularly when the patient has a chronic disorder, is adaptive. That is the clinician must adapt and then readapt treatment type, combinations and dose to the waxing and waning of the(More)
The recent explosion of high dimensionality in datasets for several domains has posed a serious challenge to existing Bayesian network structure learning algorithms. Local search methods represent a solution in such spaces but suffer with small datasets. MMHC (MaxMin Hill-Climbing) is one of these local search algorithms where a first phase aims at(More)