Alpha version. Supports only binomial classification problems.
h2o.psvm( x, y, training_frame, model_id = NULL, validation_frame = NULL, ignore_const_cols = TRUE, hyper_param = 1, kernel_type = c("gaussian"), gamma = 1, rank_ratio = 1, positive_weight = 1, negative_weight = 1, disable_training_metrics = TRUE, sv_threshold = 1e04, fact_threshold = 1e05, feasible_threshold = 0.001, surrogate_gap_threshold = 0.001, mu_factor = 10, max_iterations = 200, seed = 1 )
x  (Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. 

y  The name or column index of the response variable in the data. The response must be either a binary categorical/factor variable or a numeric variable with values 1/1 (for compatibility with SVMlight format). 
training_frame  Id of the training data frame. 
model_id  Destination id for this model; autogenerated if not specified. 
validation_frame  Id of the validation data frame. 
ignore_const_cols 

hyper_param  Penalty parameter C of the error term Defaults to 1. 
kernel_type  Type of used kernel Must be one of: "gaussian". Defaults to gaussian. 
gamma  Coefficient of the kernel (currently RBF gamma for gaussian kernel, 1 means 1/#features) Defaults to 1. 
rank_ratio  Desired rank of the ICF matrix expressed as an ration of number of input rows (1 means use sqrt(#rows)). Defaults to 1. 
positive_weight  Weight of positive (+1) class of observations Defaults to 1. 
negative_weight  Weight of positive (1) class of observations Defaults to 1. 
disable_training_metrics 

sv_threshold  Threshold for accepting a candidate observation into the set of support vectors Defaults to 0.0001. 
fact_threshold  Convergence threshold of the Incomplete Cholesky Factorization (ICF) Defaults to 1e05. 
feasible_threshold  Convergence threshold for primaldual residuals in the IPM iteration Defaults to 0.001. 
surrogate_gap_threshold  Feasibility criterion of the surrogate duality gap (eta) Defaults to 0.001. 
mu_factor  Increasing factor mu Defaults to 10. 
max_iterations  Maximum number of iteration of the algorithm Defaults to 200. 
seed  Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to 1 (timebased random number). 
if (FALSE) { library(h2o) h2o.init() # Import the splice dataset f < "https://s3.amazonaws.com/h2opublictestdata/smalldata/splice/splice.svm" splice < h2o.importFile(f) # Train the Support Vector Machine model svm_model < h2o.psvm(gamma = 0.01, rank_ratio = 0.1, y = "C1", training_frame = splice, disable_training_metrics = FALSE) }