rvm  R Documentation 
The Relevance Vector Machine is a Bayesian model for regression and
classification of identical functional form to the support vector
machine.
The rvm
function currently supports only regression.
## S4 method for signature 'formula' rvm(x, data=NULL, ..., subset, na.action = na.omit) ## S4 method for signature 'vector' rvm(x, ...) ## S4 method for signature 'matrix' rvm(x, y, type="regression", kernel="rbfdot", kpar="automatic", alpha= ncol(as.matrix(x)), var=0.1, var.fix=FALSE, iterations=100, verbosity = 0, tol = .Machine$double.eps, minmaxdiff = 1e3, cross = 0, fit = TRUE, ... , subset, na.action = na.omit) ## S4 method for signature 'list' rvm(x, y, type = "regression", kernel = "stringdot", kpar = list(length = 4, lambda = 0.5), alpha = 5, var = 0.1, var.fix = FALSE, iterations = 100, verbosity = 0, tol = .Machine$double.eps, minmaxdiff = 1e3, cross = 0, fit = TRUE, ..., subset, na.action = na.omit)
x 
a symbolic description of the model to be fit.
When not using a formula x can be a matrix or vector containing the training
data or a kernel matrix of class 
data 
an optional data frame containing the variables in the model. By default the variables are taken from the environment which ‘rvm’ is called from. 
y 
a response vector with one label for each row/component of 
type 

kernel 
the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:
The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. 
kpar 
the list of hyperparameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. For valid parameters for existing kernels are :
Hyperparameters for user defined kernels can be passed through the
kpar parameter as well. In the case of a Radial Basis kernel function (Gaussian)
kpar can also be set to the string "automatic" which uses the heuristics in

alpha 
The initial alpha vector. Can be either a vector of length equal to the number of data points or a single number. 
var 
the initial noise variance 
var.fix 
Keep noise variance fix during iterations (default: FALSE) 
iterations 
Number of iterations allowed (default: 100) 
tol 
tolerance of termination criterion 
minmaxdiff 
termination criteria. Stop when max difference is equal to this parameter (default:1e3) 
verbosity 
print information on algorithm convergence (default = FALSE) 
fit 
indicates whether the fitted values should be computed and included in the model or not (default: TRUE) 
cross 
if a integer value k>0 is specified, a kfold cross validation on the training data is performed to assess the quality of the model: the Mean Squared Error for regression 
subset 
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) 
na.action 
A function to specify the action to be taken if 
... 
additional parameters 
The Relevance Vector Machine typically leads to sparser models then the SVM. It also performs better in many cases (specially in regression).
An S4 object of class "rvm" containing the fitted model. Accessor functions can be used to access the slots of the object which include :
alpha 
The resulting relevance vectors 
alphaindex 
The index of the resulting relevance vectors in the data matrix 
nRV 
Number of relevance vectors 
RVindex 
The indexes of the relevance vectors 
error 
Training error (if 
...
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
Tipping, M. E.
Sparse Bayesian learning and the relevance vector machine
Journal of Machine Learning Research 1, 211244
https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf
ksvm
# create data x < seq(20,20,0.1) y < sin(x)/x + rnorm(401,sd=0.05) # train relevance vector machine foo < rvm(x, y) foo # print relevance vectors alpha(foo) RVindex(foo) # predict and plot ytest < predict(foo, x) plot(x, y, type ="l") lines(x, ytest, col="red")
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