Mail Filtering Algorithm Based on the Feedback Correction Probability Learning

Xiao Yun Zou

doi:10.4028/www.scientific.net/AMR.756-759.1013

Paper Titles

Research and Application on Technical Implementation Architecture in University Informatization Planning
p.993

The Application of Network Anomaly Mining Technology in Campus Network Information Security
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A Validity Study on the Electronic Commerce Mode and Informatization Construction in the Perspective of Value Chain
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Research and Application on Optimization of Multi-Thread Download Technology for Enhanced Search Engine
p.1008

Mail Filtering Algorithm Based on the Feedback Correction Probability Learning
p.1013

Design and Application of the Network Distance Teaching System
p.1017

Research and Implementation of Key RFID Technology in Poultry Products Traceability System
p.1021

The Design and Implement of the Safety Production Management & Quality Trace System for Poultry
p.1026

Research on a Sensor Network Communication Optimization Algorithm
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Mail Filtering Algorithm Based on the Feedback...

Mail Filtering Algorithm Based on the Feedback Correction Probability Learning

Abstract:

With the popularity of the Internet, e-mail with its fast and convenient advantages has gradually developed into one of the important communication tools in people's lives. However, the problem of followed spam is increasingly severe, it is not only the dissemination of harmful information, but also waste of public resources. To solve this problem, the author proposed a mail filtering algorithm based on the feedback correction probability learning. The feedback correction probability training has less feedback learning data and use error-driven training in order to achieve a high classification effect. The experiment also tested the idea.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

1013-1016

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.1013

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Online since:

September 2013

Authors:

Xiao Yun Zou

Keywords:

Feedback Probability Learning, Probability, Spam

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References

[1] K. Schneider, A Comparison of Event Models for Naive Bayes Anti-Spain E-Mail Filtering[A]. In: Proc. 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2003) [C]. Budapest, Hungary. 2003. 4: 307一314.

DOI: 10.3115/1067807.1067848

Google Scholar

[2] S.S. Keerthi，K. Shevade，C. Bhattachayya et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design[J]. Neural Computation . 13(3), (2001).

DOI: 10.1162/089976601300014493

Google Scholar

[3] Lin Chunfu, Wang Shengde. Fuzzy Support Vector Machines[J]. IEEE Transactions on Neural Networks, (2002).

Google Scholar

[4] Huang Hanpang, Liu Yihung. Fuzzy Support Vector Machines for Pattern Recognition and Data Mining [J]. International Journal of Fuzzy Systems, 4(3), (2002).

Google Scholar

[5] Li Kunlun, Huang Houkuan. An Architecture of Active Learning SVMs for Spam. Signal Processing, 2002 6th International Conference, 2002, Vol. 2, Aug. 26-30: 1247–1250.

DOI: 10.1109/icosp.2002.1180017

Google Scholar

[6] Rocchio J J. Relevance Feedback in Information Retrieval[A]. The Smart Retrieval System Experiments in Automatic Document Processing [C]. NeJersey: P rentice Hall Inc, 1971. 313-323.

Google Scholar