Approximations of Bayes classifiers for statistical learning by Magnus Ekdahl.

By Magnus Ekdahl.

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This means that it is not possible to divide the structure calculation evenly over the processors. The execution of such a search is pictured below. 4Ghz Intel Xeon processors with 2Gb of memory). The program can also be made to run faster if one increases the number of processors. 4Ghz Intel Xeon processors with > 3Gb of memory the execution would take at least 5 days. February 13, 2006 (13:19) 56 6 Evaluating classifications In this section evaluate will mean evaluate in respect to another classification.

Cellij in the matrix is the weight i ↔ j. With visited cells marked gray and the light edge as bold in the cache. 31 95 61 84 95 61 84 95 61 0 50 0 50 186 50 38 38 38 cache: 31 95 61 cache: 31 0 50 cache: 31 0 38 In the first step all edges to and from vertex 0 is checked. Those with the smallest weight to each remaining vertex are stored in the cache and the smallest (31) is added to F. In step 2 and 3 the cache is updated with new lower weights from and to vertex {1, 2}. The smallest in the remaining cache is added to F.

2). We call the graph a forest if (and only if) replacing all directed edges with undirected does not introduce any cycles, but there is not an undirected path between all vertices in the graph. To restrict the number of parents for each variable to a maximum of one in conjunction with optimizing for maximal mutual information was first done in [15]. [15] reduced the problem to an undirected minimum spanning tree problem (MST), thereby requiring that Score(xi |xj ) = Score(xj |xi ). 2 (equation (51)), but we can use mutual information in [15] as an approximation of equation (51) and then use MST.

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