similar to: Is there Discriminant Adaptive Nearest Neighbor classification?

Dear R users, Is there anyone who is aware of a R or S package that has Discriminant Adaptive Nearest Neighbor (DANN) classification by Hastie and Tibshirani? I have used the search services in R website but no luck. Thanks in advance! Jonathan -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Several of the methods I use for analyzing large data sets, such as WinGamma: determining the level of noise in data Relief-F: estimating the influence of variables depend on finding the k nearest neighbors of a point in a data frame or matrix efficiently. (For large data sets it is not feasible to compute the 'dist' matrix anyway.) Seeing the proposed solution to "[R] distance

Yo me he apuntado y me parece bien tanto la idea de reunirse, como la de comentar a través de la lista. >________________________________ > De: "r-help-es-request@r-project.org" <r-help-es-request@r-project.org> >Para: r-help-es@r-project.org >Enviado: Viernes 10 de enero de 2014 12:00 >Asunto: Resumen de R-help-es, Vol 59, Envío 5 > > >Envíe los

Is fast nearest-neighbor functionality available in R? I was thinking of something along the lines of what's currently in S+SPATIALSTATS. Thanks for any information anyone might have on this. - MZ

Hello, is there an implementation available for a shared nearest neighbor (SNN) clustering algorithm? //Jay

https://bugs.freedesktop.org/show_bug.cgi?id=94817 Bug ID: 94817 Summary: Nearest neighbor scaling? Product: xorg Version: unspecified Hardware: Other OS: All Status: NEW Severity: enhancement Priority: medium Component: Driver/nouveau Assignee: nouveau at

I've written the function below to simulate the mean 1st through nth nearest neighbor distances for a random spatial pattern using the functions nndist() and runifpoint() from spatsat. It works, but runs relatively slowly - would appreciate suggestions on how to speed up this function. Thanks. --Dale library(spatstat) sim.nth.mdist <- function(nth,nsim) { D <- matrix(ncol=nth,

hi, I have a dataset (the netflix dataset) which is basically ~18k columns and well variable number of rows but let's assume 25 thousand for now. The dataset is very sparse. I was wondering how to do kmeans/nearest neighbors or kernel density estimation on it. I tired using the spMatrix function in "Matrix" package. I think I'm able to create the matrix but as soon as I pass

Hola a todos, Gracias por avisar Carlos. Intentaré formar un grupo en Logroño. Belén Cillero Jiménez Técnico de Estadística Instituto de Estadística de La Rioja bcillero en larioja.org o?s?? ol ??d???s s???? ou ,so?u??s?p sop??lns?? s??snq ?S ________________________________________ De: r-help-es-bounces en r-project.org [r-help-es-bounces en r-project.org] en nombre de r-help-es-request

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

This update adds an intercept option (by popular request) - now one can fit a model without an intercept Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net" regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast. The current list of models covered are:

Hi, Having dipped my toe into R a few times over the last year or two, in the last few weeks I've been using it more and more; I'm now a thorough convert. I've just joined the list, because although it's great, I do have this problem... I'm using linear discriminant analysis for binary classification, and am happy with the classification performance using predict(). What

Hello! I need some help with the linear discriminant analysis in R. I have some plant samples (divided into several groups) on which I measured a few quantitative characteristics. Now, I need to infer some classification rules usable for identifying new samples. I have used the function lda from the MASS library in a usual fashion: lda.1 <- lda(groups~char1+char2+char3, data=xxx) I'd

Hi there fellow R-users, Does anyone know if there is a package for k nearest neighbours prediction as opposed to classification? I have found the package knncat but can't see a way to adjust it to predict a continuous variable. Any help would be great, Regards Wayne Jones KSS Ltd Seventh Floor St James's Buildings 79 Oxford Street Manchester M1 6SS England Company

** High Priority ** Hello R users My issue is both theorical and technical. I would like to run a penalised discriminant analysis with the fda() function, but I don''t know all the details of splines theory. I try on the example of the phonems from the article "Penalised Discriminant Analysis" of Hastie, Buja and Tibshirani 1994 : 5 groups and 256 variables. The 256

You could break your 3 class problem into several (2 or 3) 2 class problems, and then use Andy's suggestion (see the CART book). There are several ways to break the problem into 2 class problems, and several ways to combine the resulting classifiers. Tom Dietterich, Jerry Friedman, Trevor Hastie and Rob Tibshirani, among others, have articles on the question, in places like Annals of

Yo estoy esperando que empiece... Me parece fenomenal utilizar la lista, si al resto de gente (no apuntados) no le parece que le damos mucho la lata... Un saludo. Isidro > -----Mensaje original----- > De: r-help-es-bounces en r-project.org [mailto:r-help-es-bounces en r- > project.org] En nombre de Carlos J. Gil Bellosta > Enviado el: viernes, 10 de enero de 2014 11:18 > Para:

Short course: Statistical Learning and Data Mining Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel Palo Alto, CA Feb 26-27, 2004 This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we rely increasingly more on data

Short course: Statistical Learning and Data Mining Trevor Hastie and Robert Tibshirani, Stanford University Georgetown University Conference Center Washington DC September 20-21, 2004 This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we

Short course: Statistical Learning and Data Mining Trevor Hastie and Robert Tibshirani, Stanford University Sheraton Hotel, Palo Alto, California February 24 & 25, 2005 This two-day course gives a detailed overview of statistical models for data mining, inference and prediction. With the rapid developments in internet technology, genomics and other high-tech industries, we rely