VOL. 5, NO. 8, August 2015 ISSN ARPN Journal of Science and Technology All rights reserved.

Transcription

1 Applcato of SVM Model the Evaluato of the SMEs echologcal Iovato Capablt Xao-L Wag Assoc. Prof., School of Ecoomcs & Maagemet, Zhogua Uverst of echolog, Zhegzhou, Cha sx ABSRAC Applg the SVM model, the artcle establshed scetfc ad complete evaluato dex sstem of the techologcal ovato capact of SMEs, mal cludg: ovato decso-mag ablt, resource put capablt, research ad developmet ablt, the maufacturg capact, maret maretg ablt, ovato maagemet ablt from sx aspects. Based o them, the artcle coducted a obectve ad accurate measuremet ad evaluato o the techologcal ovato capact of SMEs Hea Provce Cha. Kewords: SVM Model, SME, echologcal Iovato, Evaluato, Idex Sstem. INRODUCION Evaluato of SMEs techologcal ovato capablt, o the oe had, t eeds to sthesze evaluato dex of techologcal ovato capact ad get eterprse s overall techologcal ovato capablt to reveal the status of eterprse s techologcal ovato. O the other had, t eeds to fd the formato from the comprehesve evaluato results, ad the advatage ad dsadvatage of SMEs techologcal ovato to provde decso bass for the eterprse ad goverme Evaluato dex sstem of the techologcal ovato capact of SMEs s a dex sstem whch s composed b several dfferet parameters, ad these dexes are mult-level ad complex. Evaluato of SMEs techologcal ovato capablt essetall s a multobect, mult-factor ad mult-level techologcal comprehesve evaluato problem. herefore, the approprate evaluato model s the e factor to udge whether the evaluato result s scetfc, obectve ad far. At preset, the wdel studed ad appled evaluato model mal cludes comprehesve fuzz evaluato model, eural etwor evaluato model, etc. he applcato of these models reles o a large umber of hstorcal samples. Comprehesve evaluato model s based o the ecessar expertse, ad eural etwor evaluato model stll lacs ufed mathematcal theor, whch caot mae breathrough determg etwor structure, provdg explaato of algorthm, solvg the problem over-learg or lac-learg, ad local mmum po O the bass of statstcal learg theor, Vap et al. developed a ew d of geeral mache learg method whch s called Support Vector Mache (SVM []. SVM has successfull solved the problem of the hgh dmeso ad local extremum. Usg large terval factor, SVM cotrols the trag process of learg mache to choose the classfcato hperplae wth the largest classfcato terval, whch s also called the optmal hper plae (I the separable case, relaxato factor s troduced to cotrol emprcal rs. I the case, classfcato requremet s met ad the hghest geeralzato ablt s edowed. he process of loog for optmal hperplae evetuall trasformed to Quadratc Programmg (QP, theoretcall, t gets the global optmal soluto. o deal wth the olear classfcato problem, SVM s dfferet from tradtoal mache learg, t puts space to hgh dmesoal feature space, ad stll uses large terval factor to loo for maxmum separato hperplae the hgh dmesoal feature space [2]. I fact, the hperplae the hgh dmesoal feature space correspods to the olear classfcato plae of the put space. he optmzato process of SVM does t coduct hgh dmesoal feature space; fact, t trasforms er product operato of the hgh dmesoal feature space to erel fucto operato of the orgal space so that t avods the dffcult of dealg wth problem the hgh dmesoal feature space. From the perspectve of classfcato, ths artcle apples SVM model to the comprehesve evaluato of SMEs techologcal ovato capact, ad obtas promet effects. 2. HE MODEL CONSRUCION OF SMES ECHNOLOGICAL INNOVAION CAPACIY EVALUAION 2. he Prcple ad Algorthm of SVM SVM s a d of mache learg method based o statstcal learg theor. O the bass of lmted sample formato, t compromses emprcal rs ad cofdece rage to mmze the structure rs ad get the best learg geeralzato ablt. SVM s especall sutable for lmted samples, ad t ca theoretcall obta the global optmal pot ad esure good geeralzato ablt of learg mache. Besdes, t has othg to do wth calculato complext ad sample dmeso. herefore, recetl SVM s wdel used patter recogto, fuctoal approxmato, regresso estmate, etc. [3] 388

2 he core dea of SVM s to establsh a optmal classfcato plae as a decso surface, ad accuratel dvde the samples to two categores. he optmal classfcato plae requres that classfcato surface ot ol ca separate the two correctl (trag error rate s 0, but also mae the largest classfcato terval. We ca use the followg covex quadratc programmg to descrbe the lear separable sample set,,2,...,, x R d ( x,, {, } : For the udvded lear sample set, that s, some trag sample ca t meet the model s (Formula- costrat codto, we troduce a slac varable, 0 the same as addg a pealt term to the obectve fucto, that s, we compromse largest classfcato terval ad maxmum error-classfcato sample, ad the optmal classfcato plae we get s called the geeralzed optmal classfcato plae [4]. he model (Formula- s trasformed to 2 m ( w w w w (Formula- 2 2 s. ( w x b,,2,..., I the formula-, w s model parameter, b s classfcato threshold. o solve the above problem, we get the optmal classfcato plae, ( w x b 0, whch maes the mmum, ad the marg s. 2 / w (w Usg Wolf Dual Program, we ca trasform model (Formula- to gettg the dual problem: max Q( x x 2 s. 0 0,,2,..., (Formula-2 hs s a quadratc programmg problem about Lagrage s multpler, ad t has a uque soluto. Accordg to the KK (the Karush-Kuh-ucer, we ca prove that correspodg of most samples s 0, ad ol a part (usuall a few of s ot 0. Ad the few samples are support vectors. Solvg the model (Formula- 2, we get the optmal classfcato fucto: f ( x sg( w sg( sg( sv x x x b Ad the followg, b ( w x x x b x b x, sv (Formula-3 (Formula-4 he varable sv s support vector dex se Accordg to the postve ad egatve of f (x, we ca udge the categor of sample x. t m ( w w w C( 2 s. ( w x b,,2,..., (Formula-5 o be coveet, we usuall tae pealt dex. C s a costat whch s greater tha 0, ad t cotrols the degree of pushmet to wrog pots sample. t Smlar to the process of the lear separable, geeralzed optmal classfcato s dual problem s as follows max Q( x x 2 s. 0 0 C,,2,..., (Formula-6 hrough the above process, we ca see that whe the orgal problem s trasformed to the dual problem, computatoal complext o loger depeds o space dmeso, ad ol depeds o the umber of samples or the umber of support vector, whch maes t possble to effectvel solve the hgh dmeso problems. As the optmzato fucto ad the classfcato fucto ol refer to the er product operato of trag samples, accordg to dest fuctoal theor, we ca adopt er product erel fucto K x, x whch meets Mercer ( codto, to realze the er product of the trasformato space. I that case, f we adopt approprate er product erel fucto the optmal classfcato plae, we ca realze the lear classfcato of certa olear trasformato [5]. At ths momet, the obectve fucto s trasformed to: max Q( K( x, x (Formula-7 2 he correspodg optmal classfcato fucto s trasformed to f ( x sg( sg( sv K( x, x b K( x, x b (Formula-8 Ad 389

3 b ( w x K( x, x sv (Formula-9 I geeral, SVM trasforms the put space to a hgh dmesoal space frstl b usg olear trasformato whch s defed b er product erel fucto, ad the tres to get the geeralzed optmal separatg hperplae ths space [6] Calculato Process of the Model O the bass of collectg related data of evaluato of SMEs techologcal ovato capablt, uder learg the collected SMEs techologcal ovato capablt evaluato data for the SVM classfcato model, ad the we udge techologcal ovato capablt evaluato data of the specfc SMEs. I that wa, we ca get the techologcal ovato ablt of the evaluated eterprses, ad select the SMEs whch have strog techologcal ovato capablt ad good growth propert, to help decso maers choose prort support ad good fudg growth SMEs, ad realze the purpose of cultvatg growg SMEs. 3. EMPIRICAL ANALYSIS OF HENAN SMES ECHNOLOGICAL INNOVAION CAPABILIY EVALUAION 3. he Source of the Data ad the Selecto of Evaluato Idexes I order to mae the model applcable, we eed to stud eough hstorcal data to predct the model. I order to get eough hstorcal data, ths stud uses vestgato, telephoe, E-mal, formato quer ad other was to mar some Hea SMEs techologcal ovato capablt, ad gets some actual data. From the actual obtaed data, the capabltes of the vestgated SMEs techologcal ovato are hgh level. he reaso ma be that the vestgato, the vestgated eterprses are ot wllg to show ther actual codto. It s ecessar to have suffcet data of varous levels to support the model. Cosultg relevat experts ad combg the actual codto of some eterprses wth the obtaed actual data, we effectvel expad the samples of the stud ad esure the adequac of the model samples. able s the scorg table used the vestgato. We should score a certa SME accordg to the above dex ad refer to the crtera: good 80-00, geeral 60-80, lght problem 40-60, serous problem 40 ad below. After that, we should mae comprehesve evaluato to the eterprse s techologcal ovato capablt. Ad the ratg stadards are good, geeral ad poor. he total data of samples of the stud s 20, whle the frst 00 samples are learg samples, ad the last 20 samples are verfcato samples. able 2 s part of the learg samples whch are gotte b actual vestgato, expert cosultato ad reasoable predcto. I table 2, + stads for good, 0 stads for geeral, ad - stads for poor. he data the table s dvded to 8, whch correspods to the prevous 8 dexes. 3.2 Calculato of the Data hs stud coducts aalog calculato b Lb SVM 2.83 software whch s developed b Dr. Chh-Je L of awa Uverst. Frst, the ormalzato processg of learg samples, ext, the model lears the ormalzed data to get the correspodg support vector. Coduct ormalzato processg of the verfcato samples, ad the evaluate them accordg to the leared data to get the evaluato resul he output value of the evaluato result are +, 0 or -, whch respectvel stad for good, geeral or poor of the SMEs techologcal ovato capablt. able 3 s part of the output data through aalog computato. he evaluato of the 20 verfcato sample shows that 7 tems ft, ad 3 tems do t f I geeral, the accurac of the model evaluato s 85%. From table 3, we ca see that the overall sample data, the evaluato result v correspodg to samples shows the eterprse techologcal ovato capablt s good, geeral or poor. he fal evaluato result obtaed b aalog computato s as table 4. Uder the premse that guaratees the accurac of evaluato, the evaluato results show that the techologcal ovato capabltes of Hea SMEs the samples are ot hgh geeral. Good, geeral ad poor respectvel accout for 46.67%, 34.7% ad 9.7%. It shows that 53.33% of the eterprses eed to tap potetal wth the dex sstem, ad coduct techologcal ovato cultvato ad costructo. he govermet decso-mag bod also ca develop the correspodg drecto of sstem ad polc. 3.3 Bref Aalss of the Calculato Results hrough the model valdato, we ca see that the accurac of model evaluato s 85%. It ca be see that SMEs ovato capact evaluato based o SVM s relable. hus t bascall verfes that the dex sstem of ths artcle s obectve ad scetfc. I fact, evaluatg SME s techologcal ovato capact, the model has a great superort compared wth other methods. Frst of all, the evaluato bass of the model s to stud actual data, tr to master the heret law of SMEs techologcal ovato, ad the mae obectve evaluato to the eterprses. Secod, the evaluato of the model has strog flexblt. Whe the model lears adequate actual data, marg a eterprses obectvel accordg to the dex sstem, t 390

4 ca evaluate accordg to the grade, ad output evaluato result qucl. I ths wa, we ca clearl ow the level of the eterprse techologcal ovato capact. 4. CONCLUSION Restrcted b factors such as moe, tme ad so o, the research o Hea SMEs techologcal ovato capablt evaluato model s stll ts prelmar stage, at the ext step, we eed to explore further the followg aspects: a. Settg of model calculato dex. he selecto of SMEs techologcal ovato capablt valuato dex s ver mporta hs research carred out the prelmar desg of related dex. Actuall, desg of dex eeds ot ol sold theoretcal foudato, but also eough practce foudato. I the ext step of stud, we eed to combe wth the actual stuato of Hea SMEs to mae further desg of the dex selecto. b. Collecto of the orgal data. Learg of the Model requres eough ad real raw data, ad ol ths wa, the model ca get suffcet evaluato bass. Restrcted b factors such as moe, tme ad cooperato degree of the vestgated eterprses, the data collected ths stud s ot suffcet, ad data of the true ad low techologcal ovato capact eterprses are especall suffce I order to mae the model fucto better, we eed to collect suffcet ad effectve relevat data. ACKNOWLEDGEMEN has for supportg b the Gudace S& Pla Proect of the extle Idustr Federato echolog, Cha (203064; the Ke S& Research Proect of the Educato Departmet of Hea, Cha (3A63022; the Gudace S& Pla Proect of the extle Idustr Federato echolog, Cha (204075; the Ke S& Research Proect of the Educato Departmet of Hea, Cha (4A630020; Humat ad Socal Scece Research Plag Proect of the Departmet of educato of Hea, Cha(204-q-394, ad thas all the research members for ther great cooperato wor as well. REFERENCES [] Hu Jaq, the tegrato of archves resources to promote the Regoal Sharg of [J]. Archves ad costructo, 2008, ( [2] Lu Zhepeg, Zhag Ng. he applcato of cloud computg techolog the archves [J]. he Lata world: the secod half, 200, (8 [3] Lv Yuazh. "Cloud" shared servce mode of atoal archves formato resource [J]. Stud of archval scece, 20, (4. [4] ao Shulog., Regoal dgtal archves based o cloud computg [J]. Chese archves, 203, (2. [5] Wee. Research o costructo of the Dgtal archves based of cloud computg [J]. Archves ad costructo, 20, (. AAAI Coferece o Weblogs ad Socal Meda (ICWSM, Ma 200. AUHOR PROFILE Xao-L Wag, 975, male, School of Ecoomcs ad Maagemet, Zhogua Uverst of echolog (Hea Zhegzhou , Assocate Drector, Assocate Professor, the research drecto s Eterprse Iovato Maageme able : he scorg table used the vestgato Eterprse ame Level of comprehesve evaluato excellet good medum poor NO. Idex ame Score Kowledge capact S 2 Character ablts2 3 Iteral resources S3 4 Exteral resources S4 5 R&D proect plag ad maagemet S5 6 research ad developmet group S6 7 Cooperatg wth other departmets S7 8 Degree of the maufacturg sector to partcpate ovato S8 9 Level of producto equpmet S9 0 Worers techcal level S0 Qualt ad cost maagemet S 2 Maret vestgato ad stud ablt S2 3 he maretg level S3 4 sellg etwor S4 5 after-sales servce S5 6 Iovato strateg formulato S6 7 cetve mechasm S7 8 terface maagemet S8 39

5 able 2: Sample data No categor S S2 S3 S4 S5 S6 S7 S8 S9 S0 S S2 S3 S4 S5 S6 S7 S able 3: he output data NO. V Y Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y0 Y Y2 Y3 Y4 Y5 Y6 Y7 Y able 4: Evaluato results Level of scece ad techologcal ovato capact quatt rato good % geeral % poor % 392