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SwarmSVM (version 0.1)

alphasvm: Support Vector Machines taking initial alpha values

Description

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.

Print alphasvm object

Summary alphasvm object

Print summary.alphasvm object

Usage

alphasvm(x, ...)


"alphasvm"(formula, data = NULL, ..., subset, na.action = stats::na.omit, scale = FALSE)


"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, ..., subset, na.action = stats::na.omit)


"print"(x, ...)


"summary"(object, ...)


"print"(x, ...)

Arguments

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
formula
a symbolic description of the model to be fit.
data
an optional data frame containing the variables in the model. By default the variables are taken from the environment which 'svm' is called from.
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 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.)
scale
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.
y
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).
type
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.
kernel
the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type.
linear:
$u'*v$

polynomial:
$(gamma*u'*v + coef0)^degree$

radial basis:
$exp(-gamma*|u-v|^2)$

sigmoid:
$tanh(gamma*u'*v + coef0)$

degree
parameter needed for kernel of type polynomial (default: 3)
gamma
parameter needed for all kernels except linear (default: 1/(data dimension))
coef0
parameter needed for kernels of type polynomial and sigmoid (default: 0)
cost
cost of constraints violation (default: 1)---it is the ‘C’-constant of the regularization term in the Lagrange formulation.
nu
parameter needed for nu-classification
class.weights
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.
cachesize
cache memory in MB (default 40)
tolerance
tolerance of termination criterion (default: 0.001)
epsilon
epsilon in the insensitive-loss function (default: 0.1)
shrinking
option whether to use the shrinking-heuristics (default: TRUE)
cross
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
probability
logical indicating whether the model should allow for probability predictions.
fitted
logical indicating whether the fitted values should be computed and included in the model or not (default: TRUE)
alpha
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.
mute
a logical value indicating whether to print training information from svm.
object
An object of class alphasvm

Details

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.

References

Examples

Run this code
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])

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