Capacitive empirical risk function-based bag-of-words and pattern classification processes

Abstract

This paper proposes capacitive bag-of-words (Cap BoW) modeling and capacitive pattern classification processes for improved generalization ability. The Cap BoW and capacitive pattern classification are realized by introducing the notion of the capacitive empirical risk function (CERF). The CERF is used as a cost function to build a capacitive pattern classification process. The resulted capacitive pattern classification is used in the classification stage of the bag-of-words process, realizing thereby the Cap BoW process. The use of the CERF in building the capacitive pattern classification and Cap BoW processes is demonstrated to achieve simultaneous reduction to the empirical risk function (ERF) and confidence interval, reducing potential overfitting and enhancing the generalization ability of considered models. To have a thorough evaluation, two groups of experiments are carried out. The first group addresses the capacitive pattern classification process for 4 datasets. The second group addresses a more holistic impact of the CERF by using Cap Bow model to build capacitive dual ergodicity limits-based bag-of-words (Cap DEL-BoW) process, which is applied to 5 image datasets. In both groups of experiments, remarkable enhancement is demonstrated with the use of CERF-based pattern classification and Cap BoW processes. Comparison with corresponding conventional non-capacitive models demonstrates the tangible enhancement with the use of CERF-based models.