ramsvm {ramsvm}

The classifier for Reinforced Angle-Based Multicategory Support Vector Machines (RAMSVMs).

Description

A function that provides the RAMSVMs classifier for linear learning, polynomial learning, and kernel learning.

Usage

ramsvm(x, y, lambda, gamma = 0.5, weight = NULL, kernel = "linear", 
       kparam = NULL, large = FALSE, epsilon = NULL, warm = NULL, 
       nb.core=NULL)

Arguments

x

The x matrix for the training dataset. Columns represent the covariates, and rows represent the instances. There should be no NA/NaN values in x.

y

The labels for the training dataset.

gamma

The convex combination parameter of the loss function.

weight

The weight vector for each observation. By default, the program uses equal weights for all observations.

lambda

The user specified lambda values.

kernel

The kernel for classification.

kparam

The kernel parameter. If kernel=linear, this option is ignored. For kernel=polynomial, it is the order of the polynomial functions. For kernel=gaussian, it is the Gaussian kernel parameter.

large

Whether the number of observations is large in the data. If TRUE, then the algorithm will split the data set into several parts and train on each part to provide a warm start for the entire data training. This option aims to enhance the computational speed.

epsilon

Convergence threshold in coordinate descent circling algorithm. The smaller epsilon is, the more accurate the final model is, and the more time it takes for calculation. Default is (0.0001*number of observations*number of classes).

warm

A matrix that contains the warm start for slack variables alpha. This option is especially useful when the user wishes to obtain the classifier with higher level accuracy (smaller epsilon) or with a different lambda, if the warm start is available from an existing ramsvm output.

nb.core

The number of threads to use for parallel computing. If null, the code will automatically detect and use the number of CPU cores. This option is used only when large=TRUE.

Value

An object of class ramsvm is returned.

If kernel=linear, this S4 object contains the following:

x

A copy of the input covariate matrix.

y

A copy of the input labels.

y.name

The class names of y.

k

Number of classes in the classification problems.

gamma

A copy of the convex combination parameter of the loss function.

weight

The weight vector for each observation.

lambda

The lambda vector of all lambdas in the solution path.

beta

A list of matrices containing the estimated parameters of the classification function. Each matrix in the list corresponds to the lambda value in the solution path in order. For one single matrix, the rows correspond to a specific predictor.

beta0

A list of the intercepts of the classification function. Each vector in the list corresponds to the lambda in the solution path in order.

epsilon

Convergence threshold in coordinate descent circling algorithm.

call

The call of ramsvm.

If kernel != linear the S4 object also contains the following:

kernel

The kernel for classification.

kparam

The kernel parameter.

Author(s)

Chong Zhang, Yufeng Liu, and Shannon Holloway

References

C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016). Reinforced Angle-based Multicategory Support Vector Machines. Journal of Computational and Graphical Statistics, 25, 806-825.

See Also

predict

Examples

data(iris)
ramsvm(x = as.matrix(iris[,-5]),
       y = iris[,5],
       lambda = 0.2,
       kernel="gaussian")