I have submitted the proposal, please check it once and provide your feedback.
Sarthak On Fri, Mar 25, 2016 at 2:01 PM, Dmitry Baryshnikov <bishop....@gmail.com> wrote: > Hi Sarthak, > > Thank you for you note, but I already wrote: > > > Don't wait for anybody with proposal. The new GSoC site is right > place to discuss proposals. > > So I expected to see and comment, if needed, your proposal on this site. > Let me remind you the site - https://summerofcode.withgoogle.com/ > > Best regards, > Dmitry > > 25.03.2016 10:17, sarthak agarwal пишет: > > The deadline is today. > > Sarthak > > On Thu, Mar 24, 2016 at 1:52 AM, sarthak agarwal < <sarthak0...@gmail.com> > sarthak0...@gmail.com> wrote: > >> Hello Dmitry, >> >> I fixed the bug (I guess). >> Now coming to my proposal for GSoC, So I was thinking of working on >> project #4 *Auto-detection of EPSG codes from incomplete WKT.* >> >> What I understood from the project is that we need to predict the EPSG >> code of certain files on the basis of some attributes which are available >> in the file. >> >> The attributes can be extracted from the file for which I read this >> <http://www.gdal.org/osr_tutorial.html#querying_coordinate_system>. >> >> Now to solve this problem I thought a lot of methods but I think the best >> way to solve it will be using machine learning. >> >> The way ML will handle this problem is as follows- >> >> 1. We need to find the EPSG code for a file (testing data) >> 2. We have a file with some attributes (projections,datum,etc ). >> 3. We need to the guess the best suitable class for that file(EPSG) >> 4. Also, we have many files for which we know the attributes and the >> corresponding class (training data). >> >> This problem is now translated into an ML problem which can be solved >> using the following models- >> >> 1. Bayesian Stastics >> <https://en.wikipedia.org/wiki/Posterior_probability> >> >> where, >> posteriror probability = probability of this file have EPSG code 'a'. >> prior probability = probability of occurence of EPSG code 'a'. >> >> likelihood probablity = cases where we saw such attributes when the EPSG >> code is 'a'. >> >> >> 2. or we can use a simple knn where k is the number of possible EPSG code >> and the dimension of the feature vector is the number of possible >> attributes. we need to the find a valid and promising weight function). >> >> >> 3. We can use multi-class SVM. >> >> 4. any other suggestion from the community regarding the possible choice >> of the algo. >> >> I am thinking of actually implementing all these algo(may add algo in >> future depending upon the suggestion) and select the algo which gives the >> best performance among all of them. >> >> Please provide me feedback on my proposal and suggestion if I can >> add/change anything. >> And since very less time is left in the deadline, I would like to convert >> it into proposal ASAP with your help. >> >> Regards, >> Sarthak >> >> >
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