search for: perceptrons

can u explain me, how it works your code??? please. i´m also doing a simple perceptron for homework on R and i dont know where to start. [[alternative HTML version deleted]]

Dear experts, I'm new in R and trying to learn by writing a version of the Perceptron Algorithm. How can I tell in the code below to stop the iteration when the condition in the "for loop" is not satisfied for all training examples? Thanks in advance for your help! ## Generate a linearly separable data set in R2 sample <- as.data.frame(cbind(runif(n=100),runif(n=100)))

Dear Prof. Ripley, you mention the theory of perceptrons. Could you please point me to an introduction paper or book? Thanks in previous, Dominik > -----Original Message----- > From: ripley at stats.ox.ac.uk [mailto:ripley at stats.ox.ac.uk] > Sent: dimanche, 10. novembre 2002 18:55 > To: Ben Liblit > Cc: r-help at stat.math.ethz.ch >...

Hello, I'm implementing a custom Haskell-to-LLVM compiler, and in my experimentation, noticed that GHC is much slower than clang certain examples, such as the ackermann function. However, from reading their respective IRs (Cmm for GHC and LLVM for clang), I don't really see much of a difference. Here is a link to the numbers. (n, m) are the parameters to the ackermann function

Hello, I am Mudit Raj Gupta, fourth year student of M.S. (Hons.) Chemistry and B.E. (Hons.) Electronics and Instrumentation at BITS-Pilani ( http://www.bits-pilani.ac.in/). I am interested in *Machine Learning and Computaional Intelligence*. I have an interest in implementing various existing algorithms and developing modified/new algorithms related to machine learning and computational

Hy, I try to give an example of overfitting with multi-layer perceptron. I have done following small example : library(nnet) set.seed(1) x <- matrix(rnorm(20),10,2) z <- matrix(rnorm(10),10,1) rx <- max(x)-min(x) rz <- max(z)-min(z) x <- x/rx z <- z/rz erreur <- 10^9 for(i in 1:100){ temp.mod <- nnet(x=x,y=z,size=10,rang=1,maxit=1000) if(temp.mod$value<erreur){

Hi all, I am trying to develop a neural network(Multilayer perceptron) with the package 'NEURALNET'. I have some doubts on it, 1. Whether this procedure taken care about categorical input variables- The reason is I could not find any option to describe type of variable in the arguments? 2.The algorithm is providing input variables generalized weights like this, >

Buenas, He estado leyendo publicaciones sobre las redes neuronales ( y el deep learning) pero no consigo entender el tipo de operaciones que se hacen en cada una de las capas de las redes neuronales. Necesito alguna explicacion más para tontos. ¿Alguien entiende bien lo que hace una red neuronal? Por ejemplo, un randomForest hace el ensamble por boostrap de árbole sde clasificación. Un SVM crea

Dear R folks, Thanks to all your help before I have loaded a 1-D toy data set into R and did LDA on it. The toy data has Class=0 if value>0. > XY <-- read.table ("test.xy",header=T ) > XY X.Class value 1 0 60.4897262 2 0 32.9554489 3 -1 -53.6459189 4 0 44.4450579 . . . 998 -1 -43.4183157 999 0

Hi, As there is no (decent) way to specify a particular repo for an particular package with yum, I'm having an odd problem: Although the version numbers differ, I have 2 spamassassin pkgs in different repo's (updates and my own addons) which are seen as updates to eachother: jelsum:/root-# tail -11 /var/log/yum.log Sep 02 04:26:34 Installed: perl-Mail-SpamAssassin.i386 3.1.0-1 Sep 02

Hi all, This is as much as statistical/estimation question as an R-specific one, but here goes. I am trying to use logistic regression to predict suitability of habitats for certain plant species. The response variable is a binary one that indicates whether a particular species is found at a site on the landscape. The independent variables represent physical characteristics of the landscape

I would like to use R to perform a logistic regression with about 30,000 independent variables. That's right, thirty thousand. Most will be irrelevant: the intent is to use the regression to identify the few that actually matter. Among other things, this calls for giving glm() a colossal "y ~ ..." formula with thirty thousand summed terms on its right hand side. I build up the

CRAN (and crantastic) updates this week New packages ------------ * allan (1.0) Alan Lee http://crantastic.org/packages/allan Automates Large Linear Analysis Model Fitting * andrews (1.0) Jaroslav Myslivec http://crantastic.org/packages/andrews Andrews curves for visualization of multidimensional data * anesrake (0.3) Josh Pasek http://crantastic.org/packages/anesrake This