Abstract Microclustering refers to clustering models that produce small clusters or, equivalently, to models where the size of the clusters grows sublinearly with the number of samples. We formulate probabilistic microclustering models by assigning a prior distribution on the size of the clusters

Abstract. We present an algorithm for the 2-clustering problem with cluster size constraints in the plane assuming `1-norm, that works in O(n3 logn) time and O(n) space. Such a procedure also solves a full version of the problem, computing the optimal solutions for all possible constraints

-pecially if the networks have a highly irregular structure. More precisely, our algorithm provides graph coarsening by iteratively contracting size-constrained clusterings that are computed using a label propagation algorithm. The same al-gorithm that provides the size-constrained clusterings can also be used during

presents four modifications that allow XCS to achieve high performance even in highly size-constrained populations. By modifying the genetic algorithm trigger function, the classifier deletion-selection mechanism, the frequency of classifier parameter updates and the genetic algorithm selection function

Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing

method Hes in the energy functional that constrains the shape and size of the active contour. This paper introduces a significant enhancement over existing gradientbased snakes in the form of a modified gradient vector flow. Using the gradient vector flow, we can track leukocytes rolling at high speeds

In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic

Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given

With increasing size and complexity of the implementations of information systems, it is necessary to use some logical construct (or architecture) for defining and controlling the interfaces and the integration of all of the components of the system. This paper defines information systems

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