Distributing SOM Ensemble Training using Grid Middleware

In this paper we explore the distribution of training of self-organised maps (SOM) on grid middleware. We propose a two-level architecture and discuss an experimental methodology comprising ensembles of SOMs distributed over a grid with periodic averaging of weights. The purpose of the experiments is to begin to systematically assess the potential for reducing the overall time taken for training by a distributed training regime against the impact on precision. Several issues are considered: (i) the optimum number of ensembles; (ii) the impact of different types of training data; and (iii) the appropriate period of averaging. The proposed architecture has been evaluated in a grid environment, with clock-time performance recorded

Adaptable models and semantic filtering for object recognition in street images

The need for a generic and adaptable object detection and recognition method in images, is becoming a necessity today, given the rapid development of the internet and multimedia databases in general. This paper compares the state-of-the-art in object recognition and proposes a method based on adaptable models for detecting thematic categories of objects. Furthermore, automatically constructed semantics are used for filtering false positive objects. The classification of objects into categories is performed by the popular Adaboost. The method has been used for identifying car objects and so far has indicated not only accurate recognition performance, but also good adaptability to new objects types

A Semantic-driven adaptive architecture for large scale P2P networks

The increasing amount of online information demands for effective, scalable and accurate mechanisms to manage and search this information. Distributed semantic-enabled architectures, which enforce semantic web technologies for resource discovery, could satisfy these requirements. In this work a semantic-driven adaptive architecture is presented, aiming to improve existing resource discovery processes. The P2P network is organised in a two-layered super-peer architecture. The network formation of super-peers is a conceptual representation of the network’s knowledge, which is shaped from the information provided by the nodes using collective intelligence methods. The main focus of the paper is on the creation of a dynamic hierarchical semantic-driven P2P topology using the network’s collective intelligence. The unmanageable amounts of data are therefore transformed into a repository of semantic knowledge, transforming the network into an ontology of conceptually related entities of information collected from the resources located in the peers. Appropriate experiments have been undertaken through a case study, by simulating the proposed architecture and evaluating the results

Distributing SOM ensemble training using grid middleware

In this paper we explore the distribution of training of self-organised maps (SOM) on Grid middleware. We propose a two-level architecture and discuss an experimental methodology comprising ensembles of SOMs distributed over a Grid with periodic averaging of weights. The purpose of the experiments is to begin to systematically assess the potential for reducing the overall time taken for training by a distributed training regime against the impact on precision. Several issues are considered: (i) the optimum number of ensembles; (ii) the impact of different types of training data; and (iii) the appropriate period of averaging. The proposed architecture has been evaluated in a Grid environment, with clock-time performance recorded

Distributing SOM ensemble training using grid middleware

In this paper we explore the distribution of training of self-organised maps (SOM) on Grid middleware. We propose a two-level architecture and discuss an experimental methodology comprising ensembles of SOMs distributed over a Grid with periodic averaging of weights. The purpose of the experiments is to begin to systematically assess the potential for reducing the overall time taken for training by a distributed training regime against the impact on precision. Several issues are considered: (i) the optimum number of ensembles; (ii) the impact of different types of training data; and (iii) the appropriate period of averaging. The proposed architecture has been evaluated in a Grid environment, with clock-time performance recorded

A Semantic-driven adaptive architecture for large scale P2P networks

The increasing amount of online information demands for effective, scalable and accurate mechanisms to manage and search this information. Distributed semantic-enabled architectures, which enforce semantic web technologies for resource discovery, could satisfy these requirements. In this work a semantic-driven adaptive architecture is presented, aiming to improve existing resource discovery processes. The P2P network is organised in a two-layered super-peer architecture. The network formation of super-peers is a conceptual representation of the network’s knowledge, which is shaped from the information provided by the nodes using collective intelligence methods. The main focus of the paper is on the creation of a dynamic hierarchical semantic-driven P2P topology using the network’s collective intelligence. The unmanageable amounts of data are therefore transformed into a repository of semantic knowledge, transforming the network into an ontology of conceptually related entities of information collected from the resources located in the peers. Appropriate experiments have been undertaken through a case study, by simulating the proposed architecture and evaluating the results