We present a possibly great improvement while performing semisupervised learning tasks from training data sets when only a small fraction of the data pairs is labeled. In particular, we propose a novel decision strategy based on normalized model outputs. The paper compares performances of two popular semi-supervised approaches (Consistency Method and Harmonic Gaussian Model) on the unbalanced and balanced labeled data by using normalization of the models' outputs and without it. Experiments on

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... ext categorization problems suggest significant improvements in classification performances for models that use normalized outputs as a basis for final decision.