We present the bump mixture model, a statistical model for analog data where the probabilistic semantics, inference, and learning rules derive from low-level transistor behavior. The bump mixture model relies on translinear circuits to perform probabilistic inference, and floating-gate devices to perform adaptation. This system is low power, asynchronous, and fully parallel, and supports variou...

A discrete model, inspired by publication activity, is introduced. It includes an increasing number of objects equipped with positive weights, which also increase with time. The random evolution of the model is driven by a weight dependent dynamics in such a way that the empirical weight distribution converges weakly with probability 1, and the limit law has a regularly varying tail. The probab...

Decision-theoretic reasoning and planning algorithms are increasingly being used for mobile robot navigation, due to the signi cant uncertainty accompanying the robots' perception and action. Such algorithms require detailed probabilistic models of the environment of the robot and it is very desirable to automate the process of compiling such models by means of autonomous learning algorithms. T...

In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space speci cation.

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. sspco optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. one of the things that smart algorithms are applied to solve is the problem ...

Clustering and tessellations are basic tools in data mining. The k-means and EM algorithms are two of the most important algorithms in the Mixture Model-based clustering and tessellations. In this paper, we introduce a new clustering strategy which shares common features with both the EM and k-means algorithms. Our methods also lead to more general tessellations of a spatial region with respect...