OK, thanks. Do do you suggest to me to change anything in my application?
--- Ted Dunning <[EMAIL PROTECTED]> wrote: > > If you produce nice code, then your contribution is > almost 100% likely to be > accepted by the Mahout project. > > I can't comment on the likelihood of getting funding > from Google as part of > the summer of code. > > > On 3/29/08 11:07 AM, "Marko Novakovic" > <[EMAIL PROTECTED]> wrote: > > > OK, > > > > I decided to implement SVM because it would be > useful > > for professor from my college, who works at his > SE. > > How much is probabitity to my application be > accepted? > > > > Greetings > > > > --- Ted Dunning <[EMAIL PROTECTED]> wrote: > > > >> > >> SVM is fine but can be very expensive (and > complex) > >> for training especially > >> for text-like applications. Regularized logistic > >> regression can be just > >> about as good for document classification and is > >> much easier to implement. > >> I suspect that random forests would work very > well > >> as well. > >> > >> As a GSOC project, SVM would be a good thing to > >> implement for mahout. So > >> would all of the other algorithms. > >> > >> > >> On 3/29/08 10:47 AM, "Marko Novakovic" > >> <[EMAIL PROTECTED]> wrote: > >> > >>> I collabotate with one proffesor form my > faculty, > >>> whose phd thesis was about machine learning in > >> SE-s. > >>> He uses combination of Naive Bayes and SVM. I > >> didn't > >>> understand his solution enough. > >>> But I think that SVM is very useful and > deployable > >>> algorithm for SE-s. > >>> Do you think that I should change anything in my > >>> application. > >>> > >>> Greetings > >>> > >>> --- Ted Dunning <[EMAIL PROTECTED]> wrote: > >>> > >>>> > >>>> SVM is not the only solution to these problems. > >> For > >>>> many search engine > >>>> applications, it isn't even likely to be the > >> best. > >>>> Regularized logistic > >>>> regression is a strong candidate as are random > >>>> forests and boosted trees. > >>>> > >>>> Beware of any author who claims that their > >> algorithm > >>>> for machine learning > >>>> that claims to be better than all others. The > >>>> algorithm may well have some > >>>> virtues, but it is unlikely to be universal. > It > >> is > >>>> more likely that the > >>>> author who claims this simply has a limited > view > >> of > >>>> the range of things that > >>>> might need to be done. > >>>> > >>>> > >>>> On 3/29/08 10:23 AM, "Marko Novakovic" > >>>> <[EMAIL PROTECTED]> wrote: > >>>> > >>>>> The implementation of SVM algorithm at Hadoop > >>>> platform > >>>>> > >>>>> Abstract: > >>>>> > >>>>> I have been researching in Search Engines > >>>>> functionalities, like ranking, presenting > >> relevant > >>>>> page to users, etc. > >>>>> I noted that the most usable solution for > search > >>>>> engines is Support Vector Machine. > >>>>> The best solution for determination relevant > >> page > >>>>> ranking for user based search result is SVM. > >>>>> Reference to this problem is article: > >>>>> T. Joachims, F. Radlinski: "Search Engines > that > >>>>> Laerning from Implicit Feedback," IEEE > Computer, > >>>>> August 2007, pp 38 > >>>>> According to SVM is very complex algorithm, > >> which > >>>> has > >>>>> a lot of operations, > >>>>> I decided to implement SVM algorithm at Hadoop > >>>>> platform. > >>>>> > >>>>> Dear Apache, > >>>>> > >>>>> My Idea: > >>>>> > >>>>> I have idea to implement model and solution > for > >>>>> retrieving relevant ranking Web pages driven > by > >>>> user's > >>>>> past behavior. > >>>>> According to SE-s have a lot of crawled Web > >> pages, > >>>>> this operation must be realized distributed if > >> we > >>>> want > >>>>> to obtain results in real time and have fresh > >>>> learned > >>>>> database. > >>>>> So we should paralelize all algorithms, which > >> are > >>>> used > >>>>> for processing Web pages. > >>>>> So I decided to implement the most used and > >>>> exploited > >>>>> algorithm in machine learning, deployed in > >>>> operating > >>>>> Web pages. > >>>>> I also, choose SVM algorithm because it is > very > >>>>> complex algorithm for implementation > >>>>> and I like temptations and I am not affraid of > >>>> hard > >>>>> tasks. > >>>>> I tend to achieve most a big degree of > >>>> performances > >>>>> through paralelization. > >>>>> I will exploit working on this project for > >> writing > >>>> new > >>>>> article about deployment of clustering at > SE-a. > >>>>> I have prepared to this project reading > >> articles: > >>>>> [1] C. Burges, "A Tutorial on Suppot Vector > >>>> Machines > >>>>> for Pattern Recognition," Kluwer Academin > >>>> Publishers, > >>>>> Boston > >>>>> [2] R.E Fan, P.H Chen, C.J. Lin, "Working Set > >>>>> Selection Using Second Order Information for > >>>> Training > >>>>> Support Vector Machines," Journal of Machine > >>>> Learning > >>>>> Research 6 (2005), pp 18891918 > >>>>> I also have read Hadoop documentation and > >> examined > >>>>> your implementations of algoritm kMeans at > this > >>>>> platform. > >>>>> > >>>>> Methodoligies of Development: > >>>>> > >>>>> - Test Driven Development > >>>>> - Deployment ANT an JUnit > >>>>> - SDK: Eclipse > >>>>> - SVN System for Versioning > >>>>> - Javadoc > >>>>> > >>>>> About Me: > >>>>> > >>>>> My resume you can see at link > >>>>> http://atisha34.googlepages.com/. > >>>>> I also participate in some academic projects > at > >> my > >>>>> college: > >>>>> - Working at topic based Search Engine, called > >>>> Grain, > >>>>> which is in construction at my faculty. > >>>>> - Tutorial about SE-s, mentored by professor > >>>> Veljko > === message truncated === ____________________________________________________________________________________ Never miss a thing. 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