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This paper presents two clustering algorithms based on α-divergence between memberships and variables that control cluster sizes: one is for spherical data and the other for categorical multivariate data. First, this paper shows that a conventional method for vectorial data can be interpreted as the regularization of another conventional method with α-divergence. Second, with this interpretation,...
Robots are being used more and more in dangerous environments such as space and disaster areas. However, when robots are at risk in dangerous environments, the time during which robot operators can issue risk avoidance instructions is limited. Therefore, robots should be able to acquire behaviors that enable them to autonomously avoid danger. In this paper, we present a probability-based reinforcement...
Under interval-valued fuzzy environment, based on the differently implicational idea and hierarchical inference mechanism, the interval-valued universal triple I algorithm is put forward. To begin with, the interval-valued fuzzy implications and related residual pairs are researched. Furthermore, the interval-valued universal triple I principles are proposed, and the optimal solutions of the interval-valued...
Instance selection methods are a class of preprocessing techniques that have been widely studied in machine learning to remove redundant or noisy instances from a training set. The main focus of such prior efforts has been on the selection of suitable training instances to perform a classification task for crisp class labels. In this paper, we propose a novel instance selection technique termed Fuzzy...
Transportation infrastructure takes a primary role in urban development planning. To better facilitate or understand the infrastructure status and demands, a huge amount of transportation data such as traffic flow counts has been collected from numerous transportation monitoring systems. Making full use of harvested data samples to discover important patterns has become an increasingly appealing research...
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