alphasvm is used to train a support vector machine. It can be used to carry out general regression and classification (of nu and epsilon-type), as well as density-estimation. A formula interface is provided.
alphasvm(x, ...)# S3 method for formula
alphasvm(
formula,
data = NULL,
...,
subset,
na.action = stats::na.omit,
scale = FALSE
)
# S3 method for default
alphasvm(
x,
y = NULL,
scale = FALSE,
type = NULL,
kernel = "radial",
degree = 3,
gamma = if (is.vector(x)) 1 else 1/ncol(x),
coef0 = 0,
cost = 1,
nu = 0.5,
class.weights = NULL,
cachesize = 40,
tolerance = 0.001,
epsilon = 0.1,
shrinking = TRUE,
cross = 0,
probability = FALSE,
fitted = TRUE,
alpha = NULL,
mute = TRUE,
nclass = NULL,
...,
subset,
na.action = stats::na.omit
)
# S3 method for alphasvm
print(x, ...)
# S3 method for alphasvm
summary(object, ...)
# S3 method for summary.alphasvm
print(x, ...)
a data matrix, a vector, or a sparse matrix (object of class Matrix
provided by the Matrix package, or of class matrix.csr provided by the SparseM package,
or of class simple_triplet_matrix provided by the slam package).
additional parameters for the low level fitting function svm.default
a symbolic description of the model to be fit.
an optional data frame containing the variables in the model. By default the variables are taken from the environment which 'svm' is called from.
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
A function to specify the action to be taken if NAs are
found. The default action is stats::na.omit, which leads to rejection of cases
with missing values on any required variable. An alternative
is stats::na.fail, which causes an error if NA cases
are found. (NOTE: If given, this argument must be named.)
A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.
a response vector with one label for each row/component of x. Can be either a factor (for classification tasks) or a numeric vector (for regression).
svm can be used as a classification machine. The default setting for type is C-classification, but may be set to nu-classification as well.
the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type.
\(u'v\)
\((\gamma u'v + coef0)^{degree}\)
\(e^(-\gamma |u-v|^2)\)
\(tanh(\gamma u'v + coef0)\)
parameter needed for kernel of type polynomial (default: 3)
parameter needed for all kernels except linear
(default: 1/(data dimension))
parameter needed for kernels of type polynomial
and sigmoid (default: 0)
cost of constraints violation (default: 1)---it is the ‘C’-constant of the regularization term in the Lagrange formulation.
parameter needed for nu-classification
a named vector of weights for the different classes, used for asymmetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named.
cache memory in MB (default 40)
tolerance of termination criterion (default: 0.001)
epsilon in the insensitive-loss function (default: 0.1)
option whether to use the shrinking-heuristics (default: TRUE)
if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression
logical indicating whether the model should allow for probability predictions.
logical indicating whether the fitted values should be computed
and included in the model or not (default: TRUE)
Initial values for the coefficients (default: NULL).
A numerical vector for binary classification or a nx(k-1) matrix for a k-class-classification problem.
a logical value indicating whether to print training information from svm.
the number of classes in total.
An object of class alphasvm
Tong He (based on package e1071 by David Meyer and C/C++ code by Cho-Jui Hsieh in Divide-and-Conquer kernel SVM (DC-SVM) )
For multiclass-classification with k levels, k>2, libsvm uses the
‘one-against-one’-approach, in which k(k-1)/2 binary classifiers are
trained; the appropriate class is found by a voting scheme.
libsvm internally uses a sparse data representation, which is
also high-level supported by the package SparseM.
If the predictor variables include factors, the formula interface must be used to get a correct model matrix.
plot.svm allows a simple graphical
visualization of classification models.
The probability model for classification fits a logistic distribution using maximum likelihood to the decision values of all binary classifiers, and computes the a-posteriori class probabilities for the multi-class problem using quadratic optimization. The probabilistic regression model assumes (zero-mean) laplace-distributed errors for the predictions, and estimates the scale parameter using maximum likelihood.
Chang, Chih-Chung and Lin, Chih-Jen:
LIBSVM: a library for Support Vector Machines
https://www.csie.ntu.edu.tw/~cjlin/libsvm/
Exact formulations of models, algorithms, etc. can be found in the
document:
Chang, Chih-Chung and Lin, Chih-Jen:
LIBSVM: a library for Support Vector Machines
https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz
More implementation details and speed benchmarks can be found on:
Rong-En Fan and Pai-Hsune Chen and Chih-Jen Lin:
Working Set Selection Using the Second Order Information for Training SVM
https://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf
data(svmguide1)
svmguide1.t = svmguide1[[2]]
svmguide1 = svmguide1[[1]]
model = alphasvm(x = svmguide1[,-1], y = svmguide1[,1], scale = TRUE)
preds = predict(model, svmguide1.t[,-1])
table(preds, svmguide1.t[,1])
data(iris)
attach(iris)
# default with factor response:
model = alphasvm(Species ~ ., data = iris)
# get new alpha
new.alpha = matrix(0, nrow(iris),2)
new.alpha[model$index,] = model$coefs
model2 = alphasvm(Species ~ ., data = iris, alpha = new.alpha)
preds = predict(model2, as.matrix(iris[,-5]))
table(preds, iris[,5])
Run the code above in your browser using DataLab