BELM: Bayesian Extreme Learning Machine

Soria-Olivas, Emilio; Gómez-Sanchís, Juan; Jd, Martín; Vila‐Francés, Joan; Martínez, M.; Magdalena‐Benedito, Rafael; Serrano, Adela

doi:10.1109/tnn.2010.2103956

Cited by 147 publications

(70 citation statements)

References 11 publications

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“…After Huang et al proposed the ELM algorithm, it was applied widely in computational intelligence and machine learning [11][12][13][14][15][16]. However, ELM may cause overfitting when it computes an output for high-dimensional data.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Overfitting Reduction of Text Classification Based on AdaBELM

Feng

Shi

et al. 2017

Entropy

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Abstract:Overfitting is an important problem in machine learning. Several algorithms, such as the extreme learning machine (ELM), suffer from this issue when facing high-dimensional sparse data, e.g., in text classification. One common issue is that the extent of overfitting is not well quantified. In this paper, we propose a quantitative measure of overfitting referred to as the rate of overfitting (RO) and a novel model, named AdaBELM, to reduce the overfitting. With RO, the overfitting problem can be quantitatively measured and identified. The newly proposed model can achieve high performance on multi-class text classification. To evaluate the generalizability of the new model, we designed experiments based on three datasets, i.e., the 20 Newsgroups, Reuters-21578, and BioMed corpora, which represent balanced, unbalanced, and real application data, respectively. Experiment results demonstrate that AdaBELM can reduce overfitting and outperform classical ELM, decision tree, random forests, and AdaBoost on all three text-classification datasets; for example, it can achieve 62.2% higher accuracy than ELM. Therefore, the proposed model has a good generalizability.

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Section: Extreme Learning Machinementioning

confidence: 99%

Overfitting Reduction of Text Classification Based on AdaBELM

Feng

Shi

et al. 2017

Entropy

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“…As generalization, the asymptotic stability of a certain class of integrated semigroups is discussed by means of Lyapunov functionals [12]. In this case, we obtain the exponentially bounded behavior in the sense that for and (where K denotes n factorial) (16) The possible interconnection between of the equilibrium and of the anomalous transport has been discussed.…”

Section: Ifmentioning

confidence: 99%

“…These methods introduce a probability distribution on the network parameters and the committed errors. The Bayesian ELM has the advantages of both ELM and Bayesian models [16].…”

Section: Fuzzy Bayesian Computationmentioning

confidence: 99%

Investigation of Stabilities and Instabilities at Tokamak Plasma Behavior and Machine Learning with Big Data

Rastovic¹

2017

IJMTP

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We investigate the problem of stability and instability at tokamak plasma behavior. Generally, Jaynes maximum entropy method and Bayesian decision can be applied for recognizing the shape of the plasma.In the case of the power law behavior and the instabilities of plasma we introduce a new method. The maximization of mathematical expectations for events and fuzzy entropy is used for applications of fuzzy Bayesian neural networks for optimization and simulation without assumption on recurrence. In this case, it is possible to consider also the non-Gibbsian probability distribution functions with the power law case. The new calibration method for the non-equilibrium systems has been given.

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“…Recently, [13] proposed a Bayesian methodology dubbed the Bayesian ELM (BELM). Their method relies on the utilization of Bayesian linear regression as a means of obtaining a posterior distribution over the columns w j of the trainable weights matrices W .…”

Section: Relations To Existing Modelsmentioning

confidence: 99%

“…Indeed, if we consider a kernel of the form (11), the resulting expression of K r (X, X) will turn out to be essentially low-rank by construction: Expanding (16) and (17) in terms of K r (X, X) and x(t), and using the matrix inversion lemma, the expressions of the 1HNBKM predictive mean and variance can be restated in the forms (19) and (20), respectively. Therefore, our approach comprises a generalization of the BELM network [13], incorporates it as a special case, and reduces to it when a linear kernel function is considered.…”

Section: Relations To Existing Modelsmentioning

confidence: 99%

The One-Hidden Layer Non-parametric Bayesian Kernel Machine

Chatzis

Korkinof

Demiris

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

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Abstract-In this paper, we present a nonparametric Bayesian approach towards one-hidden-layer feedforward neural networks. Our approach is based on a random selection of the weights of the synapses between the input and the hidden layer neurons, and a Bayesian marginalization over the weights of the connections between the hidden layer neurons and the output neurons, giving rise to a kernel-based nonparametric Bayesian inference procedure for feedforward neural networks. Compared to existing approaches, our method presents a number of advantages, with the most significant being: (i) it offers a significant improvement in terms of the obtained generalization capabilities; (ii) being a nonparametric Bayesian learning approach, it entails inference instead of fitting to data, thus resolving the overfitting issues of non-Bayesian approaches; and (iii) it yields a full predictive posterior distribution, thus naturally providing a measure of uncertainty on the generated predictions (expressed by means of the variance of the predictive distribution), without the need of applying computationally intensive methods, e.g., bootstrap. We exhibit the merits of our approach by investigating its application to two difficult multimedia content classification applications: semantic characterization of audio scenes based on content, and yearly song classification, as well as a set of benchmark classification and regression tasks.

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BELM: Bayesian Extreme Learning Machine

Cited by 147 publications

References 11 publications

Overfitting Reduction of Text Classification Based on AdaBELM

Overfitting Reduction of Text Classification Based on AdaBELM

Investigation of Stabilities and Instabilities at Tokamak Plasma Behavior and Machine Learning with Big Data

The One-Hidden Layer Non-parametric Bayesian Kernel Machine

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