similar to: Classification of supernovae - a challenge

Hola! Este mail llegó a r-help hoy, yo lo mandó también a esta lista. Es interesante con competiciones de predicción/clasificación! ¿Alguien que quiere cooperar? Kjetil ---------- Forwarded message ---------- From: Kjetil Halvorsen <kjetilbrinchmannhalvorsen@gmail.com> Date: Fri, Jan 29, 2010 at 12:19 Subject: Fwd: [R] Classification of supernovae - a challenge To: Kjetil Halvorsen

I'd like to announce the availability of the "tsne" package. It provides the T distributed Stocastic Neighbor Embedding algorithm by van der Maaten and Hinton: 1. http://ict.ewi.tudelft.nl/~lvandermaaten/t-SNE.html 2. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. [

I'd like to announce the availability of the "tsne" package. It provides the T distributed Stocastic Neighbor Embedding algorithm by van der Maaten and Hinton: 1. http://ict.ewi.tudelft.nl/~lvandermaaten/t-SNE.html 2. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. [

Dear R-users, I would like to process some spectroscopic data with R, and I was hoping some people might have some example code on how to do this. I would like to be able to do the following things: * Detect outlier-spectra -> This can be done by using scoreplot from the pls package * Determine the range of the spectrum to be used -> For this, I should be able to calculate the regression

Hola Rubén, Totalmente de acuerdo con los consejos de Carlos. Ten en cuenta que aunque R disponga de varios (bastantes) GB de memoria, algunos de los objetos que va creando durante el proceso pueden consumirla por completo. Es decir, que aunque tu ordenador tenga 64GB de RAM, es posible que no queden 10 GB libres para almacenar un objeto (porque ya hay demasiados objetos grandes en la

As of R 1.7.0, princomp no longer accept matrices with more coloumns than rows. I'm curious: Why was this decision made? I work a lot with data where more coloumns than rows is more of a rule than an exception (for instance spectroscopic data). To me, princomp have two advantages above prcomp: 1) It has a predict method, and 2) it has a biplot method. A biplot method shouldn't be too

I am having a similar problem understanding the data structure of the "yarn" dataset described in the "[R] data structure for plsr" posts. I have spectroscopic data I'd like to run through a PLSR and have read the tutorial series, but still do not understand the data format required for the code to process my data. My current data structure consists of a .csv file read into

So I have 2 sets of data - a training data set and a test data set. I've been doing the analysis on the training data set and then using predict and feeding the test data through that. There are 114 rows in the training data and 117 in the test data and 1024 columns in both. It's actually the same set of data split into two. The rows are made of 5 different numbers. They do represent

Yo creo que el problema principal está en betapart.core(), que crea y mantiene simultáneamente en memoria varios objetos de alrededor de 1 Gb que luego combina en una lista... Probablemente es entre las líneas el código de betaper.core donde hay que ir eliminando objetos. Saludos, Marcelino El 22/12/2015 a las 18:26, Carlos J. Gil Bellosta escribió: > Hola, ¿qué tal? > > Yo no

So I thought I'd try to use the documentation on llvm backends to try to create a DLX backend. I think I've got most of the stuff for the .td files done but I've got some problems. * Do I need to represent the PC in my XXXRegisterInfo.td file; the branch instruction effects it but you can directly access it ... I'm thinking not. * In my Instruction subclasses (in

--- src/gallium/drivers/nv50/nv50_program.c | 162 +++++++++++++++++++++++++------ src/gallium/drivers/nv50/nv50_program.h | 1 + 2 files changed, 132 insertions(+), 31 deletions(-) diff --git a/src/gallium/drivers/nv50/nv50_program.c b/src/gallium/drivers/nv50/nv50_program.c index 5594560..16bf2f1 100644 --- a/src/gallium/drivers/nv50/nv50_program.c +++

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the documentation, but I get a message error I can't

I hope that someone can help me with this: I hav always used Samba by installing the binaries. That has worked well. But now I need the SSL-option - so I have to install it from the bottom. I use redhad 6.0 - and samba 2.0.6. First I removed the rpm of the existing installation(and deleted the smb.conf in /etc). Then I gunziped/untared the file in tmp. In the source directory I did the

Hello! I have been using Samba for 2,5 years now. I have had the same problem since I started - I can not connect the Samba server from a W98 pc when the server security level is set to USER. So I have dropped security and use SHARE instead. I can see the share from the Window-client, but when I try to double click i get an error message saying: "Write networkpassword: , Resourse:

Antes de nada, me gustaría daros las gracias por toda vuestra ayuda. He estado probando todo lo que me habéis dicho a la vez, y no hay manera, sigo teniendo el problema con el espacio. En cuanto al tamaño de la base de datos, es más grande de lo que puse, me equivoqué y puse el tamaño de una base anterior con la que estuve trabajando, la actual tiene 36866 filas x 6500 columnas. He seguido todas

Dear useRs, - I am pleased to announce that the R package OHPL is now available on CRAN (https://CRAN.R-project.org/package=OHPL). The package implements the ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <doi:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys

Dear all, I'm looking for users of the hyperSpec package for handling (hyper)spectral or spectroscopic data in R which I maintain. First of all, I made a few announcements concerning the further development which can be found in the hyperSpec-help mailing list an on which I hope to get user feedback: see http://lists.r-forge.r-project.org/pipermail/hyperspec-help/2012-July/thread.html. My

Dear useRs, - I am pleased to announce that the R package OHPL is now available on CRAN (https://CRAN.R-project.org/package=OHPL). The package implements the ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <doi:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys

Just saw this go by my twitter stream: http://staff.science.uva.nl/~delaat/sne-2009-2010/p02/report.pdf via @legeza -- bda cyberpunk is dead. long live cyberpunk.

Dear R users, I?m please to announce the available on CRAN of new package largeVis.(*) largeVis offers three major features: - A fast implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of