clusterSVM | R Documentation |
Implementation of Gu, Quanquan, and Jiawei Han. "Clustered support vector machines."
clusterSVM( x, y, centers = NULL, cluster.object = NULL, lambda = 1, sparse = TRUE, valid.x = NULL, valid.y = NULL, valid.metric = NULL, type = 1, cost = 1, epsilon = NULL, bias = TRUE, wi = NULL, verbose = 1, seed = NULL, cluster.method = "kmeans", cluster.fun = NULL, cluster.predict = NULL, ... )
x |
the nxp training data matrix. Could be a matrix or a sparse matrix object. |
y |
a response vector for prediction tasks with one value for each of the n rows of |
centers |
an integer indicating the number of centers in clustering. |
cluster.object |
an object generated from |
lambda |
the weight for the global l2-norm |
sparse |
indicating whether the transformation results in a sparse matrix or not |
valid.x |
the mxp validation data matrix. |
valid.y |
if provided, it will be used to calculate the validation score with |
valid.metric |
the metric function for the validation result. By default it is the accuracy for classification. Customized metric is acceptable. |
type |
the type of the mission for |
cost |
cost of constraints violation (default: 1).
Rules the trade-off between regularization and correct classification on data.
It can be seen as the inverse of a regularization constant.
See details in |
epsilon |
set tolerance of termination criterion for optimization. If NULL, the LIBLINEAR defaults are used, which are: |
bias |
if bias is |
wi |
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 according to the corresponding class label. |
verbose |
if set to 0, no information is printed.
If set to 1 (default), the running time and validation score (if applicable) will be printed.
If set to 2, the running time ,validation score (if applicable) and the |
seed |
the random seed. Set it to |
cluster.method |
The clusterign algorithm to use. Possible choices are
If |
cluster.fun |
The function to train cluster labels for the data based on given number of centers.
Customized function is acceptable, as long as the resulting list contains two fields named as |
cluster.predict |
The function to predict cluster labels for the data based on trained object.
Customized function is acceptable, as long as the resulting list contains two fields named as |
... |
additional parameters passing to |
svm
the svm object from LiblineaR
lambda
the parameter used.
sparse
whether the data is sparsely transformed
label
the clustering label for training data
centers
the clustering centers from teh training dataset
cluster.fun
the function used for clustering
cluster.object
the object either
cluster.predict
the function used for prediction on new data based on the object
valid.pred
the validation prediction
valid.score
the validation score
valid.metric
the validation metric
time
a list object recording the time consumption for each steps.
data(svmguide1) svmguide1.t = svmguide1[[2]] svmguide1 = svmguide1[[1]] csvm.obj = clusterSVM(x = svmguide1[,-1], y = svmguide1[,1], lambda = 1, centers = 8, seed = 512, verbose = 0, valid.x = svmguide1.t[,-1],valid.y = svmguide1.t[,1]) csvm.pred = csvm.obj$valid.pred # Or predict from the data pred = predict(csvm.obj, svmguide1.t[,-1])
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