Averaging of kernel functions

Belanche, Lluís; Tosi, Alessandra

doi:10.1016/j.neucom.2012.11.044

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…As stated in previous studies [39,40], the effectiveness of the single kernel function has been verified. According to Shiju et al [41], Proposition 1 can be obtained, which sustains the validness of the self-organizing MK learning strategy.…”

Section: Lssvm Based On Self-organizing Mk Learningsupporting

confidence: 53%

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

2019

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In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.

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Section: Lssvm Based On Self-organizing Mk Learningsupporting

confidence: 53%

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

2019

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“…e multikernel algorithm for the feature fusion in this paper chooses Aver-ageMKL that the average for kernel learning is the arithmetic average [49]. Although this algorithm is simple, it is a strong algorithm that is often better than other more advanced techniques and can achieve advantageous results [50].…”

Section: Mouse Featuresmentioning

confidence: 99%

User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

Wang

Zheng

et al. 2020

Security and Communication Networks

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In order to improve the recognition rate of users with single behavioral feature and prevent impostors from restricting an input device to avoid detection, a dual-index user authentication method based on Multiple Kernel Learning (MKL) for keystroke and mouse behavioral feature fusion was proposed in this paper. Due to the heterogeneity between the keystroke features and the mouse features, we argue that each type of features is mapped to a suitable kernel and the weights of each kernel are obtained through computing and then summed to obtain a compound kernel that implements the multifeature fusion. The dataset used in this paper was collected under complete uncontrolled condition from some volunteers by using our data collection program. The experimental results show that the proposed method can obtain the best recognition accuracy of 89.6%. Compared to the traditional methods of single feature, the dual-index method can get more stable and effective authentication. Therefore, the proposed method in this paper fully demonstrates the reliability of dual-index user authentication.

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“…In the Fixed or Heuristic family there are some very simple (but effective) solutions. In fact, in some applications, the average method (that equal to the sum of the kernels (Belanche and Tosi, 2013)) can give better results than the combination of multiple SVMs each trained with one of these kernels (Pavlidis et al., 2001). Another solution, can be the element-wise product of the kernel matrices contained in the family of basic kernels (Aiolli and Donini, 2014).…”

Section: Theorymentioning

confidence: 99%

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

et al. 2019

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Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.

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Averaging of kernel functions

Cited by 10 publications

References 10 publications

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

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