A survey of robust optimization based machine learning with special reference to support vector machines

Singla, Manisha; Ghosh, Debdas; Shukla, K. K.

doi:10.1007/s13042-019-01044-y

Cited by 18 publications

(5 citation statements)

References 154 publications

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“…Alternatively, the geometric concept of margin can be viewed as a form of regularization. Previous work has shown the equivalence between support vector machines and a robust formulation of the hinge loss classifier [12]. In this paper, we develop new robust formulations for SVM and other classifiers, which lead to further gains in out-of-sample accuracy compared to non-robust methods.…”

Section: Methodsmentioning

confidence: 99%

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Sinambela¹,

Situmorang²,

Tarigan³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.

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Section: Methodsmentioning

confidence: 99%

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Sinambela¹,

Situmorang²,

Tarigan³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…where z is the weight vector, b is the bias value, and φ(x) is the kernel function [11]. There are different types of kernel function such as linear kernel, polynomial kernel, radial basis function (RBF), Gaussian kernel, and sigmoid kernel [21].…”

Section: Methodsmentioning

confidence: 99%

Modelling the fourth wave of Covid-19 pandemic in Egypt

Moneim¹,

El-Latif²

2022

J. Math. Computer Sci.

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This paper studied the dynamics of Covid-19 in Egypt using machine learning algorithms and the epidemiological model SEIR. Among the machine learning models studied, two models showed promising results (SVR, SEGPR). Predictions of the spread of Covid-19 in the next 70 days conducted using these three approaches. The data of the fourth wave of Covid-19 in egypt taken from the WHO. The statistical measures R-Squared and RMSE used to evaluate the accuracy of these models.

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“…De Keyzer F 's study shows that privacy concerns are an individual's personal endogenous character and attitude, and a level of consumer concern about speculations' behavior, and that consumers with different character have different attitudes and levels of trust about who has access to their information and how it is used when they are in an internet environment where information is disclosed [22]. Consumers' privacy concerns negatively affect users' willingness to purchase and adopt data-enabled boosts, and AI-based data-enabled boosts threaten consumers' privacy to a large extent, and the higher the degree of personalization perceived by consumers, the more likely it is to create a creepy feeling for people, as the recommended content and products show an overly close, overly familiar connection with the consumer, which in turn reduces the consumer's willingness to purchase the data-enabled boosts, willingness to purchase for data-enabled boosts [23,24]. Therefore, this study argues that in addition to the influence of the external environment (situational privacy concerns), consumer heterogeneity (privacy concerns) is also one of the important factors that need to be included in order to explore whether different types of consumers differ in their willingness to buy for data driven boosts.…”

Section: Moderating Effects Of Privacy Concernsmentioning

confidence: 99%

The Impact of Data-Based Boosting on User Click-Through Intention Under AI Personalized Recommendations

Li,

Ali,

Guo

2024

Advances in Transdisciplinary Engineering

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As the main means of product information recommendation, data boost will have an important impact on consumers’ purchasing behavior. Although there are a lot of research on data boost at present, they mainly focus on system design and algorithm research, and pay less attention to data aid under AI personalized recommendation. In view of this, this paper studies how the data boost pair under AI personalized recommendation has an impact on consumers’ willingness to click. Through scenario simulation method, three experiments were carried out with college students as experimental objects to carry out empirical research. The results show that the data boost of AI personalized recommendation positively affects click intention; Under AI personalized recommendation, perceived benefits play an important intermediary role in consumer behavior; Privacy concerns play a moderating role in the influence mechanism of AI personalized recommendation on consumers’ click intention.

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A survey of robust optimization based machine learning with special reference to support vector machines

Cited by 18 publications

References 154 publications

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Modelling the fourth wave of Covid-19 pandemic in Egypt

The Impact of Data-Based Boosting on User Click-Through Intention Under AI Personalized Recommendations

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