Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel approach for spam filtering based on the use of Deep Belief Networks (DBNs). In contrast to conventional feedfoward neural networks having one or two hidden layers, DBNs are feedforward neural networks with many hidden layers. Until recently it was not clear how to initialize the ights of deep neural networks, which resulted in poor solutions with low generalization capabilities. A greedy layer-wise unsupervised algorithm was recently proposed to tackle this problem with successful results. In this work  present a methodology for spam detection based on DBNs and evaluate its performance on three widely used datasets.  also compare our method to Support Vector Machines (SVMs) which is the state-of-the-art method for spam filtering in terms of classification performance. Our experiments indicate that using DBNs to filter spam e-mails is a viable methodology, since they achieve similar or even better performance than SVMs on all three datasets.
['Grigorios Tzortzis', 'Aristidis Likas']
I am writing a research paper on a idea Deep Belief Networks for Spam Filtering
[{"from": "human", "value": "I am writing a research paper on a idea Deep Belief Networks for Spam Filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel approach for spam filtering based on the use of Deep Belief Networks (DBNs). In contrast to conventional feedfoward neural networks having one or two hidden layers, DBNs are feedforward neural networks with many hidden layers. Until recently it was not clear how to initialize the ights of deep neural networks, which resulted in poor solutions with low generalization capabilities. A greedy layer-wise unsupervised algorithm was recently proposed to tackle this problem with successful results. In this work  present a methodology for spam detection based on DBNs and evaluate its performance on three widely used datasets.  also compare our method to Support Vector Machines (SVMs) which is the state-of-the-art method for spam filtering in terms of classification performance. Our experiments indicate that using DBNs to filter spam e-mails is a viable methodology, since they achieve similar or even better performance than SVMs on all three datasets."}]