Sorry, these are the refs: [1] Djamel A. Zighed, Stéphane Lallich, and Fabrice Muhlenbach. Sepa- rability index in supervised learning. Principles of Data Mining and Knowledge Discovery, 2431:475–487, 2002.
[2] Supowit, K. J. (1983), "The relative neighborhood graph, with an application to minimum spanning trees", J. ACM 30 (3): 428–448, doi:10.1145/2402.322386 Do you mean an ego graph? In RNG, neighbours for which exists some other neighbour in the intersection of neighbourhoods are not considered. Is not Logistic Regresion already implemented in sklearn.linear_model.LogisticRegression? On Fri, Mar 22, 2013 at 4:58 AM, Andreas Mueller <[email protected]> wrote: > Hi Ricardo. > I think you forgot to mention what [1] and [2] are. > What is the difference between a relative neighborhood graph and a > neighborhood graph? > > To me that sounds a bit to special purpose for the moment. > We need Logistic Regression first (which might also be a good GSoC project)! > > Just my opinion though ;) > > Cheers, > Andy > > > > > On 03/22/2013 06:49 AM, Ricardo Corral C. wrote: > > Ok, this is a brief description of what I'm interested in. > > Recently, I faced a problem of evaluating the quality of a method to > obtain features from protein structures. > I adopted the approach given in [1] to measure separability of my > classes independently of my capacity of make good predictions. > This is basically a hypothesis testing of whether or not the > distribution of classes over feature vectors is somewhat random. > This test is made over the construction of a Relative Neighbourhood > Graph, which is O(n^3), thus, so prohibitive for practical use. > There is an efficient method for constructing RNG on the plane > described in [2] O(n*log(n)), but O(n^2) for a higher d dimension (in > fact O(n^2*f(d)) with f(d) <= (2*sqrt(d) +2)^d...). > > Actually, I have the test implemented, and I'm refining a speedup of > RNG construction based on the Half-Space Proximal (HSP) graph. This is > O(n^2log(n)), and there is no dependence of dimension other than time > consumed in calculating distances. > > This is made by doing RNG test over edges in HSP (attached images for > clarify this). > > Could this be of interest for sklearn users? And if so, be considered for > GSoC? > > > On Thu, Mar 21, 2013 at 12:02 PM, Andreas Mueller > <[email protected]> wrote: > > On 03/21/2013 06:56 PM, Ricardo Corral C. wrote: > > I would like to contribute with an idea different from those listed. > Is this the place to describe my proposal? > > > I think posting it on the mailing list (at least a short description) > would be a good start. > Also starting to contribute ;) > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_d2d_mar > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_d2d_mar > > > > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_d2d_mar > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > -- Ricardo Corral C. -------------------------------------------- ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_d2d_mar _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
