Garen Arevian

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This paper describes a learning news agent HyNeT which uses hybrid neural network techniques for classifying news titles as they appear on an internet newswire. Recurrent plausibility networks with local memory are developed and examined for learning robust text routing. HyNeT is described for the first time in this paper. We show that a careful hybrid(More)
We have applied several dimensionality reduction techniques to data modelling using neural network architectures for classification using a number of data sets. The reduction methods considered include both linear and non linear forms of principal components analysis, genetic algorithms and sensitivity analysis. The results of each were used as inputs to(More)
The World Wide Web has been growing rapidly in recent years, along with increasing needs for contentbased webpage filtering. But most existing filtering systems cannot easily satisfy the personalized filtering demands from different users at the same time. In this paper, a customizable instance-driven webpage filtering strategy is proposed. For different(More)
In recent years, with the massive increase in the amount of available online information on the Inter-net, a need has arisen for being able to organize and access that data in a meaningful and directed way. In this paper, special emphasis is placed on neural network approaches in implementing a learning agent for text routing. A brief summary of the various(More)
This paper focuses on symbolic transducers and recurrent neural preference machines to support the task of mining and classifying textual information. These encoding symbolic transducers and learning neural preference machines can be seen as independent agents, each one tackling the same task in a different manner. Systems combining such machines can(More)