KEYWORDS: Data modeling, Neural networks, Machine learning, System identification, Statistical modeling, Mining, Matrices, Lithium, Detection and tracking algorithms, Data processing
Nowadays, people are increasingly inseparable from electronic communication tools. Email is one of the important means of communication, but the existence of spams seriously affects the users' usage. This paper focuses on the spam classification problem in a practical context. Real email messages are collected and the classification is performed using the Dynamic_LSTM model. By comparing with algorithms of traditional machine learning as well as ordinary RNN, it is shown that the accuracy of Dynamic_LSTM is increased by 8%.In addition, it is not affected by the max-feature. The experimental results show that the Dynamic_LSTM model performs better at the classification accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.