Add to ORKGAbstract. Distributed classification aims to learn with accuracy com-parable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent en-vironment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asyn-chronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR—a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust ...
Peer-to-Peer (P2P) networks have become prevalent recently, thanks in large part to the publicity su...
Peer-to-Peer (P2P) computing is a recent hot topic in the areas of networking and distributed system...
Machine learning is increasingly met with datasets that require learning on a large number of learni...
Abstract. The goal of distributed learning in P2P networks is to achieve results as close as possibl...
Abstract. Distributed classification aims to build an accurate classifier by learning from distribut...
In the recent years the Internet users have witnessed the emergence of Peer-to-Peer (P2P) technologi...
In this paper, we discuss machine intelligence for conducting routine tasks within the Internet. We ...
This paper describes an efficient method to construct reliable machine learning applications in peer...
This Master thesis investigates the performance of the lookup mechanisms in structured and unstructu...
Structured peer-to-peer systems, or else Distributed Hash Tables (DHTs), are widely established as o...
Peer-to-Peer (P2P) networks have become prevalent recently, thanks in large part to the publicity su...
Although the support vector machine (SVM) algorithm has a high generalization property for classifyi...
We consider the problem of learning classifiers for labeled data that has been distributed across se...
In recent years, peer-to-peer (P2P) systems have emerged as a powerful networking paradigm that allo...
All existing lookup algorithms in structured peer-to-peer (P2P) systems assume that all peers are un...
Peer-to-Peer (P2P) networks have become prevalent recently, thanks in large part to the publicity su...
Peer-to-Peer (P2P) computing is a recent hot topic in the areas of networking and distributed system...
Machine learning is increasingly met with datasets that require learning on a large number of learni...
Abstract. The goal of distributed learning in P2P networks is to achieve results as close as possibl...
Abstract. Distributed classification aims to build an accurate classifier by learning from distribut...
In the recent years the Internet users have witnessed the emergence of Peer-to-Peer (P2P) technologi...
In this paper, we discuss machine intelligence for conducting routine tasks within the Internet. We ...
This paper describes an efficient method to construct reliable machine learning applications in peer...
This Master thesis investigates the performance of the lookup mechanisms in structured and unstructu...
Structured peer-to-peer systems, or else Distributed Hash Tables (DHTs), are widely established as o...
Peer-to-Peer (P2P) networks have become prevalent recently, thanks in large part to the publicity su...
Although the support vector machine (SVM) algorithm has a high generalization property for classifyi...
We consider the problem of learning classifiers for labeled data that has been distributed across se...
In recent years, peer-to-peer (P2P) systems have emerged as a powerful networking paradigm that allo...
All existing lookup algorithms in structured peer-to-peer (P2P) systems assume that all peers are un...
Peer-to-Peer (P2P) networks have become prevalent recently, thanks in large part to the publicity su...
Peer-to-Peer (P2P) computing is a recent hot topic in the areas of networking and distributed system...
Machine learning is increasingly met with datasets that require learning on a large number of learni...