Displaying 4 results from an estimated 4 matches for "supportvector".
2007 Jul 11
2
RWeka control parameters classifiers interface
...ss"
library(RWeka)
# Example: no parameter influence
mySMO =make_Weka_classifier(name="weka/classifiers/functions/SMO",class=NULL,handlers=list());
# Using control =Weka_control()
m1 =mySMO(formula=class~.,data=data[,],control=Weka_control(K="weka.classifiers.functions.supportVector.PolyKernel",E=2))
m2 =mySMO(formula=class~.,data=data[,],control=Weka_control(K="weka.classifiers.functions.supportVector.PolyKernel",E=3))
m3 =mySMO(formula=class~.,data=data[,],control=c("K","weka.classifiers.functions.supportVector.PolyKernel","E"...
2007 Aug 01
1
RWeka cross-validation and Weka_control Parametrization
...w can one perform a cross validation, -say 10fold- for a given data set and given model ?
2.) Second question
What is the correct syntax for the parametrization of e.g. Kernel classifiers interface
m1 <- SMO(Species ~ ., data = iris, control = Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=0.1))
m2 <- SMO(Species ~ ., data = iris, control = Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=1.0))
> m1
SMO
Kernel used:
RBF kernel: K(x,y) = e^-(0.01* <x-y,x-y>^2)
## should be: RBF kernel: K(x,y) = e^-(0.1* <...
2003 Jul 03
1
S4 method and S3 method with same name: potentially dangerous?
...ict.smooth.spline"
[15] "predict.smooth.spline.fit"
> setClass("RSVMState",
+ representation (
+ kernel = "character",
+ rho = "numeric",
+ supportVector = "matrix",
+ alpha = "numeric",
+ negGroup = "integer",
+ posGroup = "integer"),
+ prototype(kernel = new("character"),
+...
2017 Sep 02
0
problem in testing data with e1071 package (SVM Multiclass)
...;,header =FALSE)>attach(train)
>x <-subset(train,select=-V2)
>y <-V2 >model <-svm(V2 ~.,data =train,probability=TRUE)
>summary(model)
Call:svm(formula =V2 ~.,data =train,probability
=TRUE)Parameters:SVM-Type:C-classification SVM-Kernel:radial
cost:1gamma:0.08333333Numberof SupportVectors:12(444)Numberof
Classes:3Levels:maybe noyes
>pred <-predict(model,x)
>system.time(pred <-predict(model,x))
user system elapsed 000
>table(pred,y)y
|
|pred maybe noyes maybe 400no040yes 004>pred 123456789101112yes yes yes
yes nonononomaybe maybe maybe maybe Levels:maybe noyes...