ner-params.par

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# Sample machine learning properties file

# Choose between PERCEPTRON or MAXENT_QN
Algorithm=PERCEPTRON
Iterations=500
Cutoff=0
Threads=4

## IF MAXENT_QN is chosen, activate these parameters ##
# Costs for L1- and L2-regularization. These parameters must be larger or
# equal to zero. The higher they are, the more penalty will be imposed to 
# avoid overfitting. The parameters can be set as follows:
#    if L1Cost = 0 and L2Cost = 0, no regularization will be used,
#    if L1Cost > 0 and L2Cost = 0, L1 will be used,
#    if L1Cost = 0 and L2Cost > 0, L2 will be used,
#    if both paramters are set to be larger than 0, Elastic Net 
#       (i.e. L1 and L2 combined) will be used.
L1Cost=0.1
L2Cost=0.1

# Number of Hessian updates to store
NumOfUpdates=15

# Maximum number of objective function's evaluations
MaxFctEval=30000