AN FILTERING AGENT BASED ON SUPPORT VECTOR MACHINES

Transcription

1 BULETINUL INSTITUTULUI POLITEHNIC DIN IAŞI Publcat de Unverstatea Tehncă Gheorghe Asach dn Iaş Tomul LVI (LX), Fasc. 3, 200 SecŃa AUTOMATICĂ ş CALCULATOARE AN FILTERING AGENT BASED ON SUPPORT VECTOR MACHINES BY CONSTANTIN LAZURCĂ and FLORIN LEON Abstract. E-mal flterng has recently become an mportant ssue due to the ncreasng popularty of the electronc mal communcaton. Therefore, there s a constant need to mprove the detecton of unsolcted messages, or spam. Many researchers have appled machne learnng technques for flterng spam messages, and they were proven to be successful. In ths paper we present a spam detecton agent based on support vector machnes (SVM), one of the best classfcaton methods avalable today. We test several methods of extractng numercal features from text documents, and assess the optmal values of SVM parameters needed for ths classfcaton problem. The best results show a very good classfcaton accuracy of 94%. Key words: e-mal flterng agent, support vector machnes, classfcaton Mathematcs Subject Classfcaton: 68T50, 68T42.. Introducton Snce ts early years, Internet was most popularly used as a means of communcaton. From the begnnng, users exchanged messages n a system smlar to the postal system. Thus, e-mal servce has developed, wth all the benefts of speed and flexblty that tradtonal mal does not have. Today s e-mal servces are avalable n a wde range of choces, from servers and clents usng protocols such as POP (Post Offce Protocol) and SMTP (Smple Mal Transfer Protocol) to complex e-mal servces that work drectly n the browser. One way to make proft from electronc mal s advertsng. Many companes are wllng to pay consderable amounts of money to ensure that an advertsng message on ther products reach a certan number of users. Often the recpent of these messages has not expressed a desre to receve them and ths practce regularly prevents the effcent use of hs/her e-mal servce. Brefly stated, unwanted messages are a source of proft for the senders and a source of frustraton for the recevers.

2 44 Constantn Lazurcă and Florn Leon Presently unwanted messages, or spam, total over 80% of all messages sent to the servers of an e-mal servce provder. Out of these, 8% spam messages contan nformaton on pharmaceutcal advertsng, % contan advertsng nformaton on onlne casnos and 2.3% are attempts to obtan confdental data such as bank detals or personal nformaton from the recpent. The majorty of all spam, approxmately 80% s automatcally sent by computers nfected wth vruses. Most owners of an nfected computer do not know that ther devce s part of a spammng network. Unwanted messages n 2007 caused damage of over a hundred bllon dollars. Gven ths consderable sum, e-mal servce provders and end users always look for new ways to detect and remove unwanted messages. New methods are contnuously beng developed to detect them, but spam senders regularly mprove ther technques to overcome the detecton methods. Ths creates a contnuous battle between spammers and researchers seekng new ways to protect users from the burden of spam. Spammers have quckly learned how to pass a flter based on content. Thus, parsng researchers reached the concluson that n order to be effcent, flters based on artfcal ntellgence have to be frequently updated wth new data. Sometmes even the updates were not frequent enough to prevent all unwanted messages, as senders found technques to hde ther content. Therefore, most flters based on artfcal ntellgence are accompaned by flters based on black-lsts or other methods. 2. Spam Detecton Spam research s a fascnatng subject taken separately, but t s equally nterestng how t relates to other areas. Most spam flters use at least one method of artfcal ntellgence. Spam research has revealed defcences n current artfcal ntellgence technologes and has helped to remedy them. 2.. Content-Based Flterng By the tme spam was becomng a major problem, Mcrosoft research dvson started work n 997 on developng methods of artfcal ntellgence that could be traned to flter spam [9]. In ths approach programs are provded examples of wanted e-mal and examples of spam e-mal. A learnng algorthm s then used to automatcally fnd features of wanted e-mal n comparson wth the characterstcs of spam messages. After the tranng process s complete an ncomng message can be classfed as havng a hgh probablty to be ham (a desred e-mal), a hgh probablty of beng spam, or a value n-between. The frst attempts were relatvely smple when matched up to today s technologes, usng the naïve Bayes method based on how often a word or other features appear n a spam message and n a message that s desred.

3 Bul. Inst. Polt. Iaş, t. LVI (LX), f. 3, Algorthms based on support vector machnes can reduce by half the amount of spam passng the flters based on naïve Bayes method. These learnng processes may requre repeated adjustments of the parameters that nfluence the results, but a flter based on over messages can now be traned n less than an hour due to the contnuous development of technology. Spam flterng s not the only benefcary of the development of tranng technques of artfcal ntellgences, but also motvates new, nterestng research. For example, AI algorthms are typcally desgned to maxmze accuracy (how often ther predctons are correct). But n practce, on spam flterng, fraud detecton, and many other problems, these systems are too conservatve. Only f the algorthm s almost certan that a message s spam, the message s to be classfed as spam. Ths problem has recently led researchers to develop specal methods of tranng for these partcular stuatons. A clever technque that can reduce spam by 20% or more, developed by Scott Yh at Mcrosoft research center, nvolves the creaton of two flters. The frst s traned to dentfy the dffcult cases, and the second s only traned to classfy these cases. By focusng on such cases the overall results are mproved [5] Flterng by Usng Sender s Address Flterng technques based on message content are sometmes too easly defeated because of the numerous ways to hde content. Thus, many researchers n the feld of spam flterng have focused on aspects of spam messages whch cannot be hdden, e.g. the sender of a message, dentfed by hs/her IP address, s the most mportant of them Secure Identty Numerous attempts have been made to ntroduce cryptographcally secure dentty to e-mal, ncludng standards such as PGP and S/MIME, but none has been wdely adopted. Identty s partcularly mportant n spam flterng. Almost all spam flters have some form of safe lsts, allowng users and admnstrators to dentfy senders whose e-mals can be trusted. Tradtonal cryptographc approaches to dentty securty ressted most attacks, but they were too dffcult to mplement for practcal reasons. 3. Support Vector Machnes Support vector machnes (SVM) are classfcaton systems that use hypotheses constructed n a multdmensonal space, drven by an optmzaton algorthm derved from statstcal learnng theory [3]. Ths learnng method developed by Boser, Guyon and Vapnk [] s based on well-defned mathematcal prncples. Shortly after t was ntroduced, t quckly overcame n performance the majorty of other systems such as the classcal multlayer

4 46 Constantn Lazurcă and Florn Leon perceptron neural networks n a wde range of applcatons. Support vector machnes have become popular because of the success they had n recognzng handwrtten dgts wth an error of only.% on the test set. Due to good expermental performance, they are consdered by many researchers as the best current method of classfcaton [3]. In the support vector machne classfcaton problem we are gven n l examples ( x, y),...,( x l, yl ), wth x R and y {,} for all. The goal s to fnd a hyperplane and threshold ( w, b) that separate the postve and negatve examples wth maxmum margn, also penalzng ponts nsde the margn based on a user-selected regularzaton parameter C > 0. The SVM classfcaton problem can be restated as fndng an optmal soluton to the followng quadratc programmng problem: () 2 l mn w + C,, 2 = ζ ζ w b y ( w x + b) ζ ζ 0 =,..., l Ths formulaton s motvated by the fact that mnmzng the norm of w s equvalent to maxmzng the margn; the goal of maxmzng the margn s n turn motvated by attempts to bound the generalzaton error va structural rsk mnmzaton [8]. Due to the very large number of tranng vectors and problem dmensons, t was found that a much more effcent way of solvng ths optmzaton problem was to address ts dual form: (2) max α W ( α) = s. t. 0 α C, =,..., m m = α y ( ) l = = 0 α 2 l, j= y ( ) y ( j) αα j x ( ), x ( j) Support vector machnes use a functon Φ to frst map the examples nto a hgher dmensonal space and then construct a separatng hyperplane there. The dea s to transform the data nto a new space where the data becomes lnearly separable. Then, usng the hyperplane as a decson functon, we can classfy unseen data based on whch sde of the hyperplane they le. Transformng data wth Φ can be expensve n hgh dmensonal spaces. Instead, an SVM employs a kernel functon K whch gves the dot product of the two examples n the hgher dmensonal space wthout actually

5 Bul. Inst. Polt. Iaş, t. LVI (LX), f. 3, transformng them nto that space. Ths noton dubbed as the kernel trck, allows us to perform the Φ transformaton for purposes of classfcaton to large dmensonal spaces. One ssue wth SVMs s fndng an approprate kernel for the gven data. Most research reles on a pror knowledge to select the correct kernel and then tweaks the kernel parameters va machne learnng or tral-and-error []. We can thus apply a kernel functon to the dual form of the optmzaton problem from Eq. 2, gvng: (3) max α s. t. W ( α) = 0 α C, =,..., m m = α y ( ) l = α = 0 2 l, j= y ( ) y ( j) αα K j ( ) ( j) ( x, x ) ( ) ( j) The choce of the kernel functon K ( x, x ) and the resultant feature space determnes the functonal form of the support vectors; thus, dfferent kernels produce dfferent levels of performance. Some commonly used kernels are [4], [5] and [7]: (4) Lnear: K ( x, y) = ( x y) (5) Polynomal: K ( x, y) = ( x y) 2 2 (6) Radal Bass Functon (RBF): K( x, y) = exp( x y /(2σ )) (7) Sgmod: K ( x, y) = tanh( x y+ θ ) Sequental Mnmal Optmzaton (SMO) s an algorthm for solvng large quadratc programmng (QP) optmzaton problems, wdely used for the tranng of support vector machnes. Frst developed by John C. Platt [7], SMO breaks up large QP problems nto a seres of smallest possble problems, whch are then solved analytcally [4]. 4. Case Studes Although support vector machnes are a very good classfcaton technque, they have to be confgured for each type of problem, f the user ants the best results. For every type of classfcaton problems t was necessary to develop a specalzed support vector machne wth a specalzed radal bass 2 functon kernel of the form: K( x, y) exp( x y ) d = γ and unque parameters.

6 48 Constantn Lazurcă and Florn Leon 4.. Methodology Tranng a support vector machne s a long process requrng a large amount of data and many experments to acheve optmal results. For ths applcaton we chose to tran the machne on data freely avalable from TREC [6], a project supported by the U.S. government, prmarly amed at buldng a set of text data as a benchmark for classfcaton systems. Gordon V. Cormack founded and coordnated a specal collecton to evaluate spam flters on real e-mal messages. More mportantly, t defnes standard measures and collectons for future tests. It s based on two collectons of e-mal: Synthetc, consstng of a collecton of publshed e-mal, combned wth a set of recent, carefully modfed spam messages. The collecton can be freely dstrbuted, and researchers can test ther flters on t; Prvate, where researchers submt ther code to testers whch run t on prvate collectons and return summary results only, thus mantanng confdentalty. The results n terms of dfferentatng spam e-mals from desred e-mal on the two collectons are smlar, suggestng that conclusve tests can be performed on the synthetc data set wthout affectng the valdty of the results. Therefore, we decded to use ths publc data set for tranng and testng the support vector machne. The current verson and the one used for the tests presented n ths paper s TREC 2007 and contans over messages. The documentaton provded by Chang and Ln [2] descrbes the crossvaldaton method for fndng optmal parameters for tranng a support vector machne. The method nvolves dvdng the data nto a number of subsets, usually fve, usng one part as test data, and the others as tranng data. The method terates over dfferent values of the C parameter, whch s used to balance the desre to obtan fewer errors on the tranng set and the desre to create a more general-purpose machne and the γ parameter, whch gves a measure of the nfluence a support vector has on the data space. Standard heurstcs for teratng over the values of these parameters are: For the γ parameter the values terated should be from 2-5 to 2 3 wth 2 2 as the teraton step; For the C parameter the values terated should be from 2-5 to 2 5 wth 2 2 as the teraton step. Thus, for each combnaton of values C and γ a tranng and an evaluaton phase are performed, computng the accuracy as the number of vectors correctly classfed relatve to the total number of vectors, expressed as a percentage value. After each tranng stage the resultng accuracy s compared wth the prevous values and the values for C and γ wth the hghest precson are saved. Once the parameters C and γ that gve the best results are determned, a fnal tranng cycle s run for the whole tranng data set, and a test set s then used to evaluate the generalzaton capabltes of the model.

7 Bul. Inst. Polt. Iaş, t. LVI (LX), f. 3, To perform experments wth the TREC 2007 data set, we needed a processng method to convert text data to numercal values relevant to the support vectors machne. The establshed method to extract numercal features from text s to use a dctonary of words and count the number of tmes these words appear n the document. The dctonary contanng the words that wll be counted s loaded, and the documents are dvded nto words. When a word from the dctonary s found, the correspondng components of a vector used to represent the document s ncremented. The result s passed to the support vector machne for classfcaton. In ths case, the dctonary of terms s the complete Englsh dctonary wth all possble forms of basc terms totalng over words. Thus, the resultng data belongs to a dmensonal space. The frst approach for classfcaton usng the TREC 2007 set used crossvaldaton wth classcal heurstcs on a relatvely small subset of data. We chose, for the frst experment, 000 legtmate messages and 000 spam messages for the tranng set, and the same numbers for the test set. After runnng the algorthm over all values for parameters usng the heurstcs suggested above, we acheved 00% accuracy for tranng data suggestng a perfect classfcaton, but runnng the traned classfer on the test data resulted n an accuracy of only 64%, whch suggested the over-fttng of the tran data and a loss of generalty. A frst fndng was that the γ parameter had a negatve mpact on classfcaton accuracy, a hgher value of ths parameter resulted n a lower accuracy of the classfcaton regardless of the value of C. Therefore, we decded that for the followng experments γ would have a very low value and terate only the values of parameter C. It was also notced that most classfcaton errors were caused by false-postves,.e. the classfcaton of legtmate messages as spam. False-negatve results, the wrong classfcaton of spam as legtmate e-mal, were also found. Ths error occurred wth very long text message that apparently determned the support vector machne to gnore the fragments of text that would normally dentfy a message as spam. The concluson drawn from these results was that there were too few tranng data to construct a suffcently general model. Therefore, we decded to ncrease the number of examples to 3000 legtmate messages and 3000 spam messages, for both the tranng set and the test set. New methods to extract the numercal features of the text were used, as suggested by Sculley and Wachman [0]. They rely on a dctonary of terms lke the word countng method, but nstead of usng a dctonary of complete words they use dctonares of word fragments of 3 or 4 characters. These dctonares have been bult from the complete Englsh dctonary by extractng fragments of 3 or 4 characters and addng them to the new dctonary. Thus, we obtaned words for the dctonary of 4-character words and 5776 terms for the dctonary of 3-character words. The vectors representng the documents are bult by parsng and countng the occurrences of words from the dctonary n the document. Ths s done smlarly as for the

8 50 Constantn Lazurcă and Florn Leon method n whch we use the complete Englsh dctonary. The document s dvded nto ts component words, but nstead of lookng up the word n the dctonary as n the classcal method, the word s dvded nto 3 or 4 character fragments whch wll be looked up n the dctonary. At frst glance ths method requres more tme because each word s dvded nto 3 or 4 fragments of all possble characters whch are then looked up n the dctonary. However, ths s not true. Although every word wth more than 3 or 4 characters s dvded nto all the possble fragments, the words wth fewer characters than those present n the dctonary are gnored, and thus the tme requred for the parsng stage remans about the same as the tme needed for the classcal method. Intutvely, we could thnk that the computng tme for a tranng method that uses 4-character dctonary words to be lower than the tme requred by the classcal method, and the tme requred for tranng usng a dctonary of 3-character words should be less than the tme requred n the 4- character case. However, the opposte s true. A very advantageous aspect n workng wth support vector machnes on data vectors n a space wth a large number of dmensons s that the values of many dmensons of a vector are 0, whch greatly reduces the computng tme. For example, when usng the complete dctonary of Englsh words, t has terms, therefore the number of dmensons s But an e-mal contans an average of 00 to 000 dfferent words, whch means that the vector representng the document has the 0 value on more than 75% of ts dmensons. By decreasng the number of terms, and mplctly the number of dmensons as t s the case when usng dctonares of 3 or 4 characters, one can greatly ncrease the number of dmensons wth values dfferent from 0 for the vector representng the document. More specfcally the method whch was found to have the shortest computng tme was the one that used the full Englsh dctonary, and the most neffcent n terms of computng tme was the method that uses the dctonary of 3-character terms Results For smplcty we use the followng conventon, the method that uses the complete Englsh dctonary wll be called Full Words, the method that uses a dctonary consstng of terms of 4 characters wll be called 4-Grams, and the method that uses a dctonary consstng of terms of 3 characters wll be called 3-Grams. For the new data set bult on the bass of TREC 2007 from 3000 spam messages and 3000 legtmate messages we used a seres of successve tranngs wth the tranng set and tests wth the test set usng several values for the C parameter. The frst method used was Full Words, and ts results are presented n Fg.. There were successve tranngs usng the full set of tranng data, ncrementng the C parameter from 50 to 950 wth a step of 50.

9 Bul. Inst. Polt. Iaş, t. LVI (LX), f. 3, ,93 0,925 0,92 Accuracy 0,95 0,9 0,905 0,9 0, C Fg. Results for the Full Words method on a data set of 6000 messages. The followng experment was conducted for the same data set usng the 4-Grams method ncrementng over values from 50 to 950 wth a step of 50 for the C parameter. The results are shown n Fg. 2. 0,905 0,9 0,895 0,89 Accuracy 0,885 0,88 0,875 0,87 0,865 0, C Fg. 2 Results for the 4-Grams method on a data set of 6000 messages. A fnal experment was performed usng the 3-Grams method on the same set of data, but ncrementng the value of C from 20 to 80 wth a step of 20. The results are shown n Fg. 3. From all these graphs t can be seen that n order to acheve a hgh classfcaton accuracy the value of the C parameter must be large. It means that the margn of the decson lmt of the support vector machne has to be flexble. It can also be seen that there s a rse n the classfcaton accuracy wth ncreasng the value of C, after whch the precson s constant and then a declne follows. It s less steep than the ntal growth, but notceable. In addton, there was a dfference n executon tmes correspondng to the machne that used the 3-feature extracton methods. Tranng took 0 mn on average usng the Full Words method, 2 mn usng the 4-Grams method and 5 mn wth 3-Grams method.

10 52 Constantn Lazurcă and Florn Leon 0,9 0,905 0,9 Accuracy 0,895 0,89 0,885 0,88 0, Fg. 3 Results for the 3-Grams method on a data set of 6000 messages. Snce we wanted to get better results, a hgher classfcaton accuracy and a more general model for the support vector machne, we decded to ncrease the tranng data set to 5000 legtmate messages and 5000 spam messages. The sze of the testng set was ncreased to the same values. The frst experment was performed wth Full Words method, coverng the range from 50 to 950 wth a step of 50 for the value of C. The results are shown n Fg. 4. 0,94 C 0,92 0,9 Accuracy 0,88 0,86 0,84 0, Fg. 4 Results for the Full Words method on a data set of 0000 messages. Fg. 4 shows a cap on a farly large range of C values wthout a sgnfcant decrease. Snce we antcpated smlar results for the 4-Grams method, we decded to decrease the step to 20 and based on the results of the experments carred out on the set of 6000 messages we decded to establsh the nterval between 20 and 800. The results are shown n Fg. 5. C

11 Bul. Inst. Polt. Iaş, t. LVI (LX), f. 3, ,94 0,92 0,9 Accuracy 0,88 0,86 0,84 0,82 0, Fg. 5 Results for the 4-Grams method on a data set of 0000 messages. We performed an experment on the same data set usng 3-Grams wth the same step for teratng over the C parameter, but consderng the sharp decrease observed n the experment presented n Fg. 3, we decded to lmt the maxmum value to 400. The results are shown n Fg. 6. Accuracy 0,9 0,9 0,89 0,88 0,87 0,86 0,85 0,84 0,83 0,82 0, Fg. 6 Results for the 3-Grams method on a data set of 0000 messages. Ths last set of experments also shows the cappng and slght decrease of the accuracy for large values of the C parameter. As n the experments conducted on the data set wth 6000 messages the method used for determnng the numercal characterstcs of a document based on the complete dctonary of Englsh words gves better results than the other methods. In addton, the average computng tme of a tranng cycle usng Full Words ncreased to 20 mn for the dataset wth 0000 messages, whle for method 4-Grams t ncreased to 30 mn and 40 mn for the 3-Grams method. The tests were made on a computer equpped wth an Athlon AM processor, dual core, each wth 2200 MHz frequency, and 2 GB of RAM. Gven the notceably better results obtaned usng the Full Words method and shorter computng tme for a tranng cycle when compared wth the C C

12 54 Constantn Lazurcă and Florn Leon other methods, we chose to use only ths method for the fnal experment. The tranng data set for the last experment was ncreased to 0000 legtmate messages and 0000 spam messages, the same changes beng made on the test set. The teratons for the C parameter were made n the nterval 50 to 2000 wth a step of 50. The results are shown n Fg. 7. 0,95 0,94 0,93 0,92 Accuracy 0,9 0,9 0,89 0,88 0,87 0, Fg. 7 Results for the Full Words method on a data set of messages. Ths experment also shows same cappng of the accuracy for large values of the C parameter but a slght declne after reachng the maxmum of 94% at 550. The computng tme for a tranng cycle wth the data set of messages was 45 mn on average. Therefore, the fnal model of the support vector machne for the spam detecton agent s the one traned on the dataset of messages wth the C parameter equal to 550. It has a very good accuracy and takng nto account the large amount of data that was used, t can be consdered general enough to be used n real applcatons. Table shows an overvew of the three methods used to buld the numercal characterstcs of text documents, for an easer comparson. Table Comparson of Results Obtaned wth Three Methods of Parsng the Messages of Data Sets wth Dfferent Szes Text processng method Accuracy for 6000 tranng nstances C Accuracy for 0000 tranng nstances Accuracy for tranng nstances Full Words Grams Grams The columns ndcate the total number of e-mals used for tranng and the lnes mark the methods used to buld the numercal characterstcs of the messages. It can clearly be seen that the Full Words method based on the

13 Bul. Inst. Polt. Iaş, t. LVI (LX), f. 3, complete dctonary of the Englsh language s better than other methods based on dctonares that use words formed of fragments of 3 or 4 characters. Another mportant aspect to be noted s that for the last experment the amount of tranng data was doubled, but the accuracy ncreased by only 0.5%. Therefore, we can say wth a hgh degree of certanty that further ncreasng the number of messages of the tranng set wll not sgnfcantly mprove the classfcaton accuracy. Table 2 presents the average executon tmes of a tranng cycle for the experments performed. Table 2 Comparson of Tme Requred for a Tranng Cycle wth Three Methods of Parsng the Messages and Tranng Sets of Dfferent Szes Text processng method Tranng tme for 6000 nstances Tranng tme for 0000 nstances Tranng tme for nstances Full Words Grams Grams Conclusons Support vector machnes are one of the best classfcaton methods currently avalable (arguably the best). They are successfully appled n ndustry and research n areas such as text classfcaton, handwrtten character dentfcaton and gene classfcaton based on proten sequences. SVMs have a sold mathematcal background and they can produce very good results f properly used. Ths artcle shows that SVM can be used to obtan good results n the constantly challengng feld of flterng unwanted messages, or spam. The results were very good wth all three presented methods, but the best was obtaned usng the complete dctonary of the Englsh language n terms of both classfcaton accuracy, 94%, and n terms of computng tme. A c k n o w l e d g e m e n t s. Ths work was supported by CNCSIS- UEFISCSU, project number PNII-IDEI 36/2008, Behavoural Patterns Lbrary for Intellgent Agents Used n Engneerng and Management. Receved: July 8, 200 Gheorghe Asach Techncal Unversty of Iaş, Department of Computer Engneerng e-mal: fleon@cs.tuas.ro R E F E R E N C E S. Boser B.E., Guyon I.M., Vapnk V.N., A Tranng Algorthm for Optmal Margn Classfers. Proc. of the Ffth Annual Workshop on Comput. Learnng Theory, COLT 92, New York, NY, USA, ACM Press, 44 52, Chang C.C., Ln C.J., LIBSVM - A Lbrary for Support Vector Machnes. ntu.edu. tw/~cjln/lbsvm, 200.

14 56 Constantn Lazurcă and Florn Leon 3. Crstann N., Shawe-Taylor J., An Introducton to Support Vector Machnes and Other Kernel-Based Learnng Methods. Cambrdge Unv. Press, Herbch R., Learnng Kernel Classfers: Theory and Algorthms. The MIT Press, Cambrdge, Khan F.M., Arnold M.G., Pottenger W.M., Hardware-Based Support Vector Machne Classfcaton n Logarthmc Number Systems. IEEE Internat. Symp. on Crcuts a. Syst., Vol. 5, , * * * Test Data for Text Retreval Conferences. Natonal Insttute of Standards and Technology, Platt J.C., Fast Tranng of Support Vector Machnes Usng Sequental Mnmal Optmzaton. Advances n Kernel Methods: Support Vector Learnng, MIT Press, Cambrdge, MA, USA, , Pontl M., Rfkn R., Evgenou T., From Regresson to Classfcaton n Support Vector Machnes. Europ. Symp. on Artfcal Neural Networks, Bruges, Belgum, (999). 9. Saham M., Dumas S., Heckerman D., Horvtz E., A Bayesan Approach to Flterng Junk E-mal. Learnng for Text Categorzaton, AAAI Workshop, AAAI Techn. Report WS-98-05, Madson, WI, Sculley D., Wachman G.M., Relaxed Onlne SVMs for Spam Flterng. Proc. of the 30th Ann. Internat. ACM SIGIR Conf. on Research and Develop. n Informat. Retreval, , Sullvan K., Luke S., Evolvng Kernels for Support Vector Machne Classfcaton. Proc. of the 9th Ann. Conf. on Genetc and Evolutonary, London, England, (2007). 2. Vapnk V.N., The Nature of Statstcal Learnng Theory. Sprnger Verlag, Vapnk V.N., Chervonenks A.Ya., Theory of Pattern Recognton: Statstcal Problems of Learnng. Moscow, Nauka, * * * Sequental Mnmal Optmzaton. The Free Encyclopeda, org/wk/sequental_mnmal_optmzaton, Yh W., Goodman J., Hulten G., Learnng at Low False Postve Rates. Proc. of the Thrd Conf. on E-mal and Ant-Spam, CEAS, Mountan Vew, CA, AGENT DE FILTRARE A MESAJELOR DE BAZAT PE MAŞINI CU VECTORI SUPORT (Rezumat) Fltrarea mesajelor de e-mal a devent în ultmul tmp o problemă mportantă datortă populartăń în contnuă creştere a comuncăr prn ntermedul poşte electronce. De aceea, exstă o nevoe constantă de a îmbunătăń detecńa mesajelor nesolctate, a spamulu. MulŃ cercetător au aplcat tehnc de învăńare automată pentru fltrarea mesajelor spam ar acestea s-au dovedt încununate de succes. În acest artcol se prezntă un agent de detecńe a spam-ulu bazat pe maşn cu vector suport (engl. support vector machnes, SVM), una dn cele ma bune metode de clasfcare dsponble în prezent. Se testează câteva metode de extragere a trăsăturlor numerce dn documentele text ş se evaluează valorle optme ale parametrlor SVM necesare pentru această problemă de clasfcare. Cele ma bune rezultate ndcă o precze foarte bună a clasfcăr, de 94%.