jLibSvm: How to set up parameters for a well formed train - svm

Question

I'm trying to use jLibSvm to train biometric data, all of the same type.
There are 5 classes of data tagged with strings.
I've tried either with a MultiClassModel or with a BinaryModel, but in both case, any prediction always returns the same result, even by changing the gamma value.
Below, my code for MultiClassModel:
public static <L,SparseVector> void train(Map<SparseVector,L> examples){
MultiClassificationSVM svm = new MultiClassificationSVM(new C_SVC());
ImmutableSvmParameterPoint.Builder builder = new ImmutableSvmParameterPoint.Builder();
builder.C = 1.0;
builder.kernel = new GaussianRBFKernel(0.001);
builder.eps = 0.1f;
ImmutableSvmParameter params = builder.build();
MutableMultiClassProblemImpl problem =
new MutableMultiClassProblemImpl(String.class, null,
examples.size(), new NoopScalingModel());
problem.examples = examples;
for( SparseVector v : examples.keySet() )
problem.exampleIds.put(v, problem.exampleIds.size());
model = svm.train(problem, params);
}
And BinaryModel:
public static <L> void train(List<SparseVector> exTrue, List<SparseVector> exFalse, String label){
C_SVC svm = new C_SVC();
ImmutableSvmParameterPoint.Builder builder = new ImmutableSvmParameterPoint.Builder();
builder.C = C;
builder.kernel = new GaussianRBFKernel(gamma);
builder.eps = 0.001f;
ImmutableSvmParameter params = builder.build();
MutableBinaryClassificationProblemImpl problem
= new MutableBinaryClassificationProblemImpl(String.class, exTrue.size()+exFalse.size());
for(SparseVector v : exTrue)
problem.addExample(v, label);
String inverse = new StringLabelInverter().invert(label);
for(SparseVector v : exFalse)
problem.addExample(v, inverse);
BinaryModel bm = svm.train(problem, params);
models.put(label, bm);
}