similar to: svm in e1071 package: polynomial vs linear kernel

Dear list, In some places (for example, http://en.wikipedia.org/wiki/Support_vector_machine) , the polynomail kernel in SVM is written as (u'*v + 1)^d, while in the document of svm() in e1071 package, the polynomial kernel is written as (gamma*u'*v + coef0)^d. I am a little confused here: When doing parameter optimization (grid search or so) for polynomial kernel, does it need to tune

In e1071 package/svm default epsilon value is set to 0.1 and not 0.5 as documentation says. R

Hello list I've been studying libvorbis code and found a strange fragment in smallft.c: 38 static void drfti1(int n, float *wa, int *ifac){ 39 static int ntryh[4] = { 4,2,3,5 }; 40 static float tpi = 6.28318530717958648f; 41 float arg,argh,argld,fi; 42 int ntry=0,i,j=-1; 43 int k1, l1, l2, ib; 44 int ld, ii, ip, is, nq, nr; 45 int ido, ipm, nfm1; 46 int nl=n; 47 int nf=0;

Dear David, Dear Friends, After any running svm I receive different results of Error estimation of 'svm' using 10-fold cross validation. What is the reason ? It is caused by the algorithm, libsvm , e1071 or something els? Which value can be optimal one ? How much run can reach to the optimality.And finally, what is difference between Error estimation of svm using 10-fold cross validation

Hi all, Sorry to be bothering again with probably an easy error to fix, but I've been trying to solve the problem and haven't been able yet to do it. So I'm doing this: > dados<-read.table("b.txt",sep="",nrows=30000) >

On a CentOS 6.7 system that's been running fail2ban for a long time, we recently started seeing this: ct 28 19:00:59 <servername> fail2ban.action[17561]: ERROR iptables -w -D INPUT -p tcp --dport ssh -j f2b-SSH#012iptables -w -F f2b-SSH#012iptables -w -X f2b-SSH -- stderr: "iptables v1.4.7: option `-w' requires an argument\nTry `iptables -h' or 'iptables --help' for

Jonathan Lennox wrote: > +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) > +{ > + int32x4_t coef0 = vld1q_s32(coef32); > + int32x4_t coef1 = vld1q_s32(coef32 + 4); > + int32x4_t coef2 = vld1q_s32(coef32 + 8); > + int32x4_t coef3 = vld1q_s32(coef32 + 12); > + > + int32x4_t a0 = vld1q_s32(buf32 -

Dear friends, I have written the following lines in R console wich already exist in pdf file systemfit: data( "GrunfeldGreene" ) library( "plm" ) GGPanel <- plm.data( GrunfeldGreene, c( "firm", "year" ) ) greeneSur <- systemfit( invest ~ value + capital, method = "SUR", + data = GGPanel ) greenSur I have obtained the following incomplete

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

On Nov 23, 2015, at 12:04 PM, John Ridges <jridges at masque.com<mailto:jridges at masque.com>> wrote: Hi Jonathan. I really, really hate to bring this up this late in the game, but I just noticed that your NEON code doesn't use any of the "high" intrinsics for ARM64, e.g. instead of: int32x4_t coef1 = vmovl_s16(vget_high_s16(coef16)); you could use: int32x4_t coef1

Large for loops are slow. Try to avoid them using apply, sapply, etc. I've made the paste statements a lot shorter by using collapse. See ?paste for more info. Append.SQL <- function(x, channel){ sql="INSERT INTO logger (time, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) VALUES("d1=strptime(x[2],"%d/%m/%y %H:%M:%S %p '", d1, "' ,", paste(x[3:12],

Dear Random Forests gurus, I have a question about R^2 provided by randomForest (for regression). I don't succeed in finding this information. In the help file for randomForest under "Value" it says: rsq: (regression only) - "pseudo R-squared'': 1 - mse / Var(y). Could someone please explain in somewhat more detail how exactly R^2 is calculated? Is "mse"

Hi I am trying to tune an svm by doing the following: tune(svm, similarity ~., data = training, degree = 2^(1:2), gamma = 2^(-1:1), coef0 = 2^(-1:1), cost = 2^(2:4), type = "polynomial") but I am getting Error in svm.default(x, y, scale = scale, ...) : wrong type specification! > I have to admit I am not sure what I am doing wrong. Could anyone tell me why the

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

Hi, I am using nnet to learn transfer functions. For each transfer function I can estimate the best possible Mean Squared Error (MSE). So, rather than trying to grind the MSE to 0, I would like to use abstol to stop training once the best MSE is reached. Can anyone confirm that the abstol parameter in the nnet function is the MSE, or is it the Sum-of-Squares (SSE)? Best regards, Sam.

Hi, R guys: I'm using L-BFGS-B method of optim for minimization problem. My function called besselI function which need non-negative parameter and the besselI will overflow if the parameter is too large. So I set the constraint box which is reasonable for my problem. But the point outside the box was test, and I got error. My program and the error follows. This program depends on CircStats

Dear R-experts, Here below my R code with an error message. Can somebody help me to fix this error?? Really appreciate your help. Best, ############################################################ #?MSE CROSSVALIDATION Lasso regression? library(glmnet) ?

Hi, I am trying to integrate a function which is approximately constant over the range of the integration. The function is as follows: > my.fcn = function(mu){ + m = 1000 + z = 0 + z.mse = 0 + for(i in 1:m){ + z[i] = rnorm(1, mu, 1) + z.mse = z.mse + (z[i] - mu)^2 + } + return(z.mse/m) + } > my.fcn(-10) [1] 1.021711 > my.fcn(10) [1] 0.9995235 > my.fcn(-5) [1] 1.012727 > my.fcn(5)

If possible I'd like to produce a function that applies a formula to a column in a matrix (essentially calculating the mse) and then inserts it between values of a an array ... confusing I know, here is an example of what I'm trying to accomplish: ## create a matrix (a <- matrix(c(3,6,4,8,5,9,12,15),nrow=4)) ## get the mean of all columns (b <- apply(a,2,mean)) ## calculate the mse

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models