This explains why the threshold mask for facies is made of parallel rectangles.
Again, the threshold mask for the diagenetic levels is formed of parallel rectangles because of the use of the TGS method.
The label of a point pi is an axis-parallel rectangle of specified size.
Moreover, we improve the preprocessing time of an existing axis-parallel rectangle closest-pair query data structure from quadratic to near-linear.
This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem.
First, we propose an O(n2logn) time in-place algorithm for finding the maximum clique of the intersection graph of a set of n axis-parallel rectangles of arbitrary sizes.
In this paper, we study the problem of designing in-place algorithms for finding the maximum clique in the intersection graphs of axis-parallel rectangles and disks in R2.
Unlike classic regression approaches, which find a single equation for a given set of data, model trees partition the space of examples into axis-parallel rectangles and fit a model to each of these partitions.
In this step, the two separate threads are generated, and initial conditions and control parameters are initiated (the threads are demonstrated in Figure 1 using two parallel vertical rectangles).
The maximal layer of S is a set of points in S that are not dominated by any other point in S. Considering Q as an axes-parallel query rectangle, we design an adaptive space efficient data structure using layers of maxima (iterative maximal layers) for reporting the points in Q∩S.
The minimal axis-parallel hyper-rectangle containing all the points in the subtree defines a bounding box per subtree.
