Show simple item record

dc.contributor.authorPérez, A. 
dc.contributor.authorInza, I.
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2016-06-13T13:31:22Z
dc.date.available2016-06-13T13:31:22Z
dc.date.issued2016-01-01
dc.identifier.issn0888-613X
dc.identifier.urihttp://hdl.handle.net/20.500.11824/135
dc.description.abstractDuring the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for $k<2$. In this work, we propose the fractal tree family of algorithms which approximates this problem with a computational complexity of $\mathcal{O}(k \cdot n^2 \log n)$ in the worst case, where $n$ is the number of implied random variables and N is the size of the training set. The fractal tree algorithms construct a sequence of maximal $i$-order decomposable graphs, for $i=2,...,k,$ in $k - 1$ steps. At each step, the algorithms follow a divide-and-conquer strategy that decomposes the problem into a set of separate problems. Each separate problem is efficiently solved using the generalized Chow-Liu algorithm. Fractal trees can be considered a natural extension of the Chow-Liu algorithm, from $k = 2$ to arbitrary values of $k$, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their competitive behavior, their low computational complexity and their modularity, which allow them to implement different parallelization strategies, the proposed procedures are especially advisable for modeling high dimensional domains.
dc.formatapplication/pdf
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectApproximating probability distributions
dc.subjectBounded clique size
dc.subjectLearning decomposable models
dc.subjectMaximum likelihood problem
dc.subjectThe Chow-Liu algorithm
dc.titleEfficient approximation of probability distributions with k-order decomposable models
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.ijar.2016.03.005
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0888613X16300329
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleInternational Journal of Approximate Reasoningen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España