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 >>>>> Milutinovic: "The New Avenues in Search Engines" >>>>> presentation: >>>>> >> http://atisha34.googlepages.com/Searchengines.ppt >>>>> abstract: >>>>> >>>> >>> >> > http://atisha34.googlepages.com/TheNewAvenuesinWebSearch.docx >>>>> I should publish article driven by this >>>> presentation >>>>> at IPSI Magazine. >>>>> - Other projects in which I participate aren't >>>> related >>>>> to machine learning and search engines. >>>>> >>>>> My Interests: >>>>> - Search Engines >>>>> - Software Engineering and Test Driven >> Development >>>>> - Machine Learning >>>>> - Database Modeling and OO Design >>>>> - ERP and Business Processes >>>>> >>>>> Sincerely Yours, >>>>> Marko Novakovic >>>>> >>>>> --- Karl Wettin <[EMAIL PROTECTED]> wrote: >>>>> >>>>>> Marko Novakovic skrev: >>>>>> >>>>>> Hi Marko, >>>>>> >>>>>>> I apply for SVM algorithm at Hadoop platform. >>>>>>> I hope that I will be accepted by Google and >>>>>> Appache, >>>>>>> I am serious in intention to do this jos as >>>> great. >>>>>> >>>>>> great news! Feel free to post your proposal >> here >> > === message truncated === > > > > > ______________________________________________________________________________ > ______ > OMG, Sweet deal for Yahoo! users/friends:Get A Month of Blockbuster Total > Access, No Cost. W00t > http://tc.deals.yahoo.com/tc/blockbuster/text2.com
