Fuzzy clustering comprises a family of prototype-based clustering methods that can be formulated as the problem of minimizing an objective function. These methods can be seen as “fuzzifications” of, for example, the classical c-means algorithm, which strives to minimize the sum of the (squared) distances between the data points and the cluster centers to which they are assigned. However, it is ...

We propose and evaluate a class of objective funct ions tha t rank hypotheses for feature labels. Our approach takes into account the representation cost and qual i ty of the shapes themselves, and balances the geometric requirements against the photometr ic evidence Th is balance is essential for any system using underconstrained or generic feature models. We introduce examples of specific mod...

The effectiveness of traditional CAD optimization algorithms is proportional to the accuracy of the targeted objective functions. However, behavioral synthesis tools are not used in isolation; they form a strongly connected design flow where each tool optimizes its own objective function without considering the consequences on the optimization goals of the subsequently applied tools. Therefore,...