000			03391nam a2200313Ia 4500
001			77608
003			ft6057
005			20251124095400.0
008			190426n 000 0 eng d
040			_erda
041			_aengtag
050			_aQA76.9 C36 2017
055			_a.
082			_b2
100			_aJerhica Kim T. Canaya and Jolina P. Escolano.
245		0	_aA further enhancement of Paul Graham's Batesian algorithm applied in spam filtering
264			_a. _b. _cc2017
300			_bUndergraduate Thesis: (BSCS major in COmputer Science) -Pamantasan ng Lungsod ng Maynila, 2017.
336			_b. _atext _2rdacontent
337			_30 _b. _aunmediated _2rdamedia
338			_30 _b. _avolume _2rdacarrier
505			_aABSTRACT: Nowadays, spammers are just in a corner, sending random and irrelevant mails to our-e-mails, considering that we need to check our received mails for the day. These spam mails may contain malicious words or attachment, links that redirects you to an unwanted website, and some links contain viruses that can harm your computer without even knowing it. These are threats to users, spammers can get information just by simply opening the mail they sent. This research paper presents a variation of token to consider that may use in filtering and number of token to test. This will be beneficial to all who’s using the email to send messages, these may prevent the user in having unnecessary files and viruses attached to the email. We have done manual simulations and computerized simulation to know the possible result of mail stricter. The Paul Graham’s Bayesian algorithm is a machine learning algorithm that trains and classifies a data or a token with different score, we therefore conclude that considering HTML tags and multiple word as token use to determine whether it is from a spam ail or non-spam mail is much effective. In applying this algorithm to spam filtering stricter, we can distinguish whether our received mail is a spam or not.
506			_a5
520			_aABSTRACT: Nowadays, spammers are just in a corner, sending random and irrelevant mails to our-e-mails, considering that we need to check our received mails for the day. These spam mails may contain malicious words or attachment, links that redirects you to an unwanted website, and some links contain viruses that can harm your computer without even knowing it. These are threats to users, spammers can get information just by simply opening the mail they sent. This research paper presents a variation of token to consider that may use in filtering and number of token to test. This will be beneficial to all who's using the email to send messages, these may prevent the user in having unnecessary files and viruses attached to the email. We have done manual simulations and computerized simulation to know the possible result of mail stricter. The Paul Graham's Bayesian algorithm is a machine learning algorithm that trains and classifies a data or a token with different score, we therefore conclude that considering HTML tags and multiple word as token use to determine whether it is from a spam ail or non-spam mail is much effective. In applying this algorithm to spam filtering stricter, we can distinguish whether our received mail is a spam or not.
526			_aF
540			_a5
655			_aacademic writing
942			_alcc _cARCHIVES _2lcc
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