Quantum algorithms can enhance machine learning in different aspects. The quantum support vector was proposed to improve the performance, which Swap Test plays a crucial role realizing classification. However, as is destructive, must be repeated preparing qubits and manipulating operations. This paper proposes based on amplitude estimation (AE-QSVM) gets rid of constraint repetitive process sav...

Abstract In this study, wavelet support vector machine (WSWM) model is proposed for daily suspended sediment (SS) prediction. The WSVM model is achieved by combination of two methods; discrete wavelet analysis and support vector machine (SVM). The developed model was compared with single SVM. Daily discharge (Q) and SS data from Yadkin River at Yadkin College, NC station in the USA were used. I...

A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We cal...

Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. Firs...

This paper proposes to use a fuzzy entropy based feature selection approach to reduce the dimensionality of DAIS hyperspectral data. To compare its performance, three other filter based feature selection approaches were used. A support vector machine was used as a classification algorithm. In order to compare various feature selection approaches with full dataset, McNemar’s test based test for ...

Analyzing the energy performance in a building is an important task in energy conservation. To accurately predict the energy consumption is difficult in practice since the building is a complex system with many parameters involved. To obtain enough historical data of energy uses and to find out an approach to analyze them become mandatory. In this paper, we propose a simulation method with the ...