Incremental learning for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.gif" display="inline" overflow="scroll"><mml:mi>ν</mml:mi></mml:math>-Support Vector Regression

Gu, Bin; Sheng, Victor S.; Wang, Zhijie; Ho, Derek L.; Osman, Said; Li, Shuo

doi:10.1016/j.neunet.2015.03.013

Cited by 404 publications

(40 citation statements)

References 24 publications

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“…An appropriate model can save time and get a satisfactory accuracy. In this paper, we studied six kinds of machine-learning algorithms, which are Random Forest, Decision Tree, SVM (Support Vector Machine) [34], KNN (k-Nearest Neighbor) [16], Naive Bayes [20] and Discriminant Analysis. These algorithms are widely used in activity recognition and are always efficient.…”

Section: Model Selectionmentioning

confidence: 99%

PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

Yang

Wang

2016

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Abstract:Human activity recognition has been a hot topic in recent years. With the advances in sensor technology, there has been a growing interest in using smartphones equipped with a set of built-in sensors to solve tasks of activity recognition. However, in most previous studies, smartphones were used with a fixed position-like trouser pockets-during recognition, which limits the user behavior. In the position-independent cases, the recognition accuracy is not very satisfactory. In this paper, we studied human activity recognition with smartphones in different positions and proposed a new position-independent method called PACP (Parameters Adjustment Corresponding to smartphone Position), which can markedly improve the performance of activity recognition. In PACP, features were extracted from the raw accelerometer and gyroscope data to recognize the position of the smartphone first; then the accelerometer data were adjusted corresponding to the position; finally, the activities were recognized with the SVM (Support Vector Machine) model trained by the adjusted data. To avoid the interference of smartphone orientations, the coordinate system of the accelerometer was transformed to get more useful information during this process. Experimental results show that PACP can achieve an accuracy over 91%, which is more effective than previous methods.

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Section: Model Selectionmentioning

confidence: 99%

PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

Yang

Wang

2016

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“…To be more precise, ν is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. In addition, with probability 1, asymptotically, ν equals to both fractions (Gu et al, 2015). Therefore, it is easier to tune parameter ν than ε-SVR.…”

Section: Equivalent Formulation Of ν-Svrmentioning

confidence: 99%

“…Unfortunately, it is rather difficult to select an appropriate C. To address this issue, Schölkopf et al (2000) proposed the ν-SVR, which uses a new parameter ν to replace the parameter C. Moreover, it is easier to tune parameter ν than C. However, compared with the dual problem of ε-SVR, two complications are introduced in ν-SVR. The first one is that the box constraints are related to C and the length of the training samples and the second one is that one more inequality constraint is introduced (Gu et al, 2015). Moreover, as proved in Gu et al (2012), it will not guarantee that a feasible updating path can always be generated.…”

Section: Introductionmentioning

confidence: 99%

A Soft Sensor Modelling of Biomass Concentration during Fermentation using Accurate Incremental Online V-Support Vector Regression Learning Algorithm

Gu¹,

Pan²

2015

American Journal of Biochemistry and Biotechnology

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In order to model real fermentation process, a soft sensor modelling of biomass concentration during fermentation using accurate incremental online ν-Support Vector Regression (ν-SVR) learning algorithm was proposed. Firstly, an accurate incremental online ν-SVR learning algorithm was proposed. This algorithm solved the two complications introduced in the dual problem based on the equivalent formulation of ν-SVR. Moreover, it addressed the infeasible updating path problem during the adiabatic incremental process by relaxed adiabatic incremental adjustments and accurate incremental adjustments. Then, the proposed algorithm is used to predict the biomass concentration of glutamic acid fed-batch fermentation process online. The results of simulation experiment showed that the soft sensor modelling of biomass concentration during fermentation using the proposed algorithm was of better generalization ability and cost less training time than that of ν-SVR.

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“…In order to effectively extract fault features of bearing from the vibration signal, many scholars have proposed many effective methods, such as short time Fourier transform (STFT), wavelet transform (WT), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), entropy, and so on [2][3][4][5]. Seker and Ayaz [6] proposed a new method to extract features from the measured vibration signals in motors subjected to accelerated bearing fluting aging and to detect the effects of bearing fluting at each aging cycle of induction motors.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Zhao

Sun

Deng

et al. 2016

Entropy

182

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Abstract:Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

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Incremental learning for ν-Support Vector Regression

Cited by 404 publications

References 24 publications

PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

A Soft Sensor Modelling of Biomass Concentration during Fermentation using Accurate Incremental Online V-Support Vector Regression Learning Algorithm

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

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