Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them accordi...

Wireless sensor networks (WSN) are networks of small, embedded devices, capable of sensing information from the environment, processing the collected data, and establishing communications among each other and with a remote server site. An important observed problem is embedding a distributed data-mining algorithm into a sensor network. The proposed solution uses local predictors on each sensor ...

The mean shift algorithm has achieved considerable success in target tracking due to its simplicity and robustness. However, the lack of spatial information may result in its failure to get high tracking precision. This might be even worse when the target is scale variant and the sequences are gray-levels. This paper presents a novel multiple subtemplates based tracking algorithm for the termin...

COMPUTATIONAL ASPECTS OF APPROVAL VOTING AND DECLARED-STRATEGY VOTING by Robert Hampton LeGrand III ADVISOR: Professor Ron K. Cytron December, 2007 St. Louis, Missouri Designing a protocol for collective decision-making is made difficult by the possibility of manipulation through insincere voting. The traditional plurality system suffers from this problem; approval voting addresses it by allowi...

Voting is the most popular replica control algorithm due to its simplicity in implementation and low communication and computing overhead. The basic idea of voting is to assign each site a vote. Majority consensus is required for each update request so that at any time only one update can be committed in the system. In order to compare and evaluate different voting algorithms, analytic models h...

Manipulation models for electoral systems are a core research theme in social choice theory; they include bribery (unweighted, weighted, swap, shift, . . . ), control (by adding or deleting voters or candidates), lobbying in referenda and others. We develop a unifying framework for manipulation models with few types of people, one of the most commonly studied scenarios. A critical insight of ou...

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is no...

The Hough transform (HT) is widely used for feature extraction and object detection. However, during the HT individual image elements vote for many possible parameter values. This results in a dense accumulator array and problems identifying the parameter values that correspond to image features. This article proposes a new method for implementing the voting process in the HT. This method emplo...

We present a local distributed algorithm for a general Majority Voting problem: different and timevariable voting powers and vote splits, arbitrary and dynamic interconnection topologies and link delays, and any fixed majority threshold. The algorithm combines a novel, efficient anytime spanning forest algorithm, which may also have applications elsewhere, with a “charge fusion” algorithm that ...

Abstract The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the forest propose an called ‘Bayesian Weighted Random Forest’(B-RF), focus on problem that inaccurate decision tree caused by same voting weights in traditional model. main idea underlying proposed model to replace supermajority of forests into weighted voting...