NAME
i.pr.model - Module to generate model from features file. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
KEYWORDS
imagery, image processing, pattern recognition
SYNOPSIS
i.pr.model
i.pr.model help
i.pr.model [-gtsn] features=string model=string [validation=string] [test=string] [npc=integer] [bagging=integer] [boosting=integer] [reg=integer] [misclass_ratio=float] [reg_verbose=integer] [progressive_error=integer] [cost_boosting=float] [weights_boosting=integer] [parallel_boosting=integer] [soft_margin_boosting=integer] [tree_stamps=integer] [tree_minsize=integer] [tree_costs=integer[,integer,...]] [svm_kernel=string] [svm_kp=float] [svm_C=float] [svm_cost=float] [svm_tol=float] [svm_eps=float] [svm_l1o=integer] [svm_maxloops=integer] [svm_verbose=integer] [nn_k=integer] [--verbose] [--quiet]
Flags:
- -g
- selected model: gaussian mixture.
- -t
- selected model: classification trees.
- -s
- selected model: support vector machines.
- -n
- selected model: nearest neighbor.
- --verbose
- Verbose module output
- --quiet
- Quiet module output
Parameters:
- features=string
- Input file containing the features (output of i.pr_features).
- model=string
- Name of the output file containing the model.
- validation=string
- Input file containing other features for the training
and for the evaluation of the performances of the model
on an independent test set (for regularized AdaBoost).
- test=string
- Input file containing other features for the evaluation of the
performances of the model on an independent test set.
- npc=integer
- Number of the principal components to be used for the model development.
If not set all the principal components will be used.
Ignored if features does not contain principal components model.
- bagging=integer
- Number of bagging models.
If bagging = 0 the classical model will be implemented.
Implemented for trees and svm only.
- Default: 0
- boosting=integer
- Number of boosting models.
If boosting = 0 the classical model will be implemented.
Implemented for trees and svm only.
- Default: 0
- reg=integer
- Number of misclassification ratio intervals.
- Default: 0
- misclass_ratio=float
- For regularized AdaBoost: misclassification ratio for
hard point shaving and compute the new model.
- Default: 1.00
- reg_verbose=integer
- For regularized AdaBoost:
- if it is set = 1 the current value of misclassification
ratio and the current error will be printed.
- if it is set to >1 the number of
loops, accuracy and the current value of misclassification ratio
will be printed.
For shaving and compute:
- if it is set >0 the numbers of samples shaved will be printed.
- Default: 0
- progressive_error=integer
- Progressive estimate of the model error
increasing the number of aggregated models
- Options: 0,1
- Default: 0
- cost_boosting=float
- Cost parameter for the implementation of cost-sensitive procedure(w in [0,2]).
w>1 results higher weight on examples of class 1.
w<1 results higher weight on examples of class -1.
w=1 corresponds to standard Adaboost.
- Default: 1.0
- weights_boosting=integer
- For boosting: if weights_boosting = 1, a file containing the evolution
of the weights associated to data points will be produced.
- Options: 0,1
- Default: 0
- parallel_boosting=integer
- For boosting: number of true boosting steps for parallel boosting.
Implemented only for trees!!
- Default: 0
- soft_margin_boosting=integer
- For boosting: if soft_margin_boosting = 1, sof margin of Ababoost
will bee used. Implemented only with trees. (Sperimental!!!!!!!!!!)
- Options: 0,1
- Default: 0
- tree_stamps=integer
- For trees: if tree_stamps = 1, a single split tree will be procuded,
if tree_stamps = 0, a classical tree will be procuded.
- Options: 0,1
- Default: 0
- tree_minsize=integer
- For trees: minimum number of examples containined
into a node for splitting the node itself
- Default: 0
- tree_costs=integer[,integer,...]
- For trees: misclassification costs for each class
- svm_kernel=string
- For svm: type of employed kernel.
- Options: gaussian,linear,2pbk
- Default: linear
- svm_kp=float
- For svm: kernel parameter (Required parameter if you are using gaussian kernel).
- svm_C=float
- For svm: optimization parameter (Required parameter).
- svm_cost=float
- Cost parameter for the implementation of cost-sensitive procedure(w in [-1,1]).
w>0 results higher weight on examples of class 1.
w<0 results higher weight on examples of class -1.
w=0 corresponds to standard SVM.
Not yet implemented (and may be it will be never implemented)
for bagging and boosting
- Default: 0.0
- svm_tol=float
- For svm: tollerance parameter.
- Default: 0.001
- svm_eps=float
- For svm: epsilon.
- Default: 0.001
- svm_l1o=integer
- For svm: leave 1 out error estimate.
- Options: 0,1
- Default: 0
- svm_maxloops=integer
- For svm: maximum number of optimization steps.
- Default: 1000
- svm_verbose=integer
- For svm: if it is set to 1 the number of loops will be printed.
- Options: 0,1
- Default: 0
- nn_k=integer
- For nn: Number of neighbor to be considered during the test phase.
- Default: 1
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