This landmark title describes, in-depth, how statistical filtering is being used by next-generation spam filters to identify and filter unwanted messages, how spam filtering works and how language classification and machine learning combine to produce remarkably accurate spam filters. Join author John Zdziarski for a look inside the brilliant minds that have conceived clever new ways to fight spam in all its nefarious forms. The smoothing algorithm is helpful to solve the unseen feature words problem due to the sparse data. And in the experiment with cross entropy extracting feature words, the performance of naive Bayes with Good-Turing algorithm is even 1.95% higher than that of Maximum Entropy model. The experimental corpora come from the data in National 863 Evaluation on text classification, and in the open test with removing the stop words, the naive Bayes performance with Good-Turing algorithm is 3.05% higher than that with Laplace, and 1.00% higher than that with Lidstone. Inspired by statistical language model, a novel approach is proposed, which applies the smoothing algorithms to naive Bayes for text classification task to overcome the unseen feature words problem.
Moreover, this problem is hardly to be solved by expanding the corpora for there is the sparse data problem in the corpora, in which the distribution of words complies with Zipf law. When applied to deal with text classification task, naive Bayes is always suffered from the unseen feature words problem.
The algorithm is effective, accurate, prevent false classification and negate spammer's innovation. However manipulated spam mail is of no effect in hybridized word stemming combined with Bayesian probability spam filter approach. The implementation of the algorithm when tested with direct and manipulated spam mail content was able to successfully identified spam mail with manipulated suspicious terms and 99% of the tested known manipulated suspicious terms spam mail were identified and classified as spam. The hybridized technique was used to detect modified suspicious terms by examining the base root of the misspelled or modified manipulated suspicious words/terms and reconverting them to the correct token or near correct token and examine as such. However, this paper proposes word stemming combined with Bayesian probability approach to regain spam-free inbox in the electronic mail infrastructure. Unfortunately, spammers deceived content based filters by coming up with sophisticated means of circumventing detective pattern of developed content filters, manipulating and rearranging spam mail suspicious terms/content to fool such filters, since content based spam filters only work effectively, if the suspicious terms are lexically and grammatically correct.
#SURGEMAIL CC SENT MESSAGES UPGRADE#
2023 Everglades Boats 273 CC, Underside of Hardtop - Match Hull Color Powder Coating to Match Hull Side Color (Gel Only) Helm Master EX Electronic Steering with Auto Pilot Canvas Package - Console Cover, Forward Console Seat Cover, Helm Seat/Leaning Post Cover Power Pole 8' - White JL Speakers LED M6 Upgrade Underwater Lights, Spectrum Quattro, Rgbw Basic Package - Garmin VHF 215 AIS, Gpsmap 8616xsv Chart Plotter, B175HW Transducer GXM 54 Sirius XM & Marine Weather Receiver the Dealership is not Responsible for any Errors or Omissions Made in the List of Standard Features.Content based spam filter prevents spam mail from successful delivery to the targeted host using Bayesian probability approach.
0 kommentar(er)
