Inductive bias represents an important factor in learning theory, as it can shape the generalization properties of a learning machine. This paper shows that biased regularization can be used as inductive bias to effectively tackle the semi-supervised classification problem. Thus, semi-supervised learning is formalized as a supervised learning problem biased by an unsupervised reference solution. The proposed framework has been tested on a malware-detection problem. Experimental results confirmed the effectiveness of the semi-supervised methodology presented in this paper.

Semi-supervised machine learning approach for unknown malicious software detection

GASTALDO, PAOLO;ZUNINO, RODOLFO;
2014-01-01

Abstract

Inductive bias represents an important factor in learning theory, as it can shape the generalization properties of a learning machine. This paper shows that biased regularization can be used as inductive bias to effectively tackle the semi-supervised classification problem. Thus, semi-supervised learning is formalized as a supervised learning problem biased by an unsupervised reference solution. The proposed framework has been tested on a malware-detection problem. Experimental results confirmed the effectiveness of the semi-supervised methodology presented in this paper.
2014
978-1-4799-3020-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/754793
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