BNS Scaling: An Improved Representation over TF.IDF for SVM Text Classification

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08-07-2008

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BNS Scaling: An Improved Representation over TF.IDF for SVM Text Classification

HPL-2007-32 (R.1) BNS Scaling: An Improved Representation over TF.IDF for SVM Text Classification - Forman, George
Keyword(s): text classification; topic identification; machine learning; feature selection; Support Vector Machine; TF*IDF text representation
Abstract: In the realm of machine learning for text classification, TF.IDF is the most widely used representation for real-valued feature vectors. Unfortunately, it is oblivious to the training class labels, and naturally scales some features inappropriately. We replace IDF with Bi-Normal Separation (BNS), wh ...
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liblinear-predict

LIBLINEAR-PREDICT(1)					      General Commands Manual					      LIBLINEAR-PREDICT(1)

NAME

       liblinear-predict - Make predictions based on a trained linear classifier model

SYNOPSIS

       linear-predict [options] test_file model_file output_file

DESCRIPTION

       liblinear-predict  uses the linear classifier model-file to make predictions for each of the samples in test_file and stores the results in
       output_file.

OPTIONS

       A summary of options is included below.

       -b (0|1)
	      Whether to output probability estimates or not (default: 0)

EXAMPLES

       Train a linear SVM using L2-loss function with linear-train(1):

		  liblinear-train data_file

       Output probability estimates (for logistic regression only):

		  liblinear-predict -b 1 test_file data_file.model output_file

SEE ALSO

       liblinear-train(1), svm-predict(1), svm-train(1)

AUTHORS

       liblinear-predict was written by the LIBLINEAR authors at National Taiwan university for the LIBLINEAR Project.

       This manual page was written by Christian Kastner <debian@kvr.at>, for the Debian project (and may be used by others).

								  March 08, 2011					      LIBLINEAR-PREDICT(1)

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