Learning and generalization characteristics of the random vector functional-link net

Pao, Yoh‐Han; Park, Gwang Hoon; Šobajić, D.J.

doi:10.1016/0925-2312(94)90053-1

Cited by 934 publications

(392 citation statements)

References 7 publications

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“…By using multi-scale sliding window detectors; all sub windows extracted from a still image and are normalized and resized. Then the two detectors cascade AdaBoost [35] deetctor and random vector functional-link net [36][37] are applied simultaneously on this candidate feature set to check if it is a pedestrian or not. Experiments with four datasets have shown that this technique outperforms other methods in terms of detection accuracy and false positive rates.…”

Section: ) Machine Learning Based Methodsmentioning

confidence: 99%

Intelligent Pedestrian Detection using Optical Flow and HOG

Ramzan¹,

Fatima²,

Raza³

et al. 2016

ijacsa

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Abstract-Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular region in front of car. But these approaches alone are not enough to differentiate humans from non-humans in dynamic environments, where background is continuously changing. We present an automated pedestrian detection system by finding pedestrians' motion patterns and combing them with HOG features. The proposed scheme achieved 17.7% and 14.22% average miss rate on ETHZ and Caltech datasets, respectively.

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Section: ) Machine Learning Based Methodsmentioning

confidence: 99%

Intelligent Pedestrian Detection using Optical Flow and HOG

Ramzan¹,

Fatima²,

Raza³

et al. 2016

ijacsa

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“…Although Huang formalized the idea of ELM algorithm [25,26], it was previously analyzed in other works [23,24]. Huang demonstrated that the ELM is a universal approximator for a wide range of random computational nodes.…”

Section: Introductionmentioning

confidence: 99%

Classifying BCI signals from novice users with extreme learning machine

2017

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Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

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“…The idea of ELM is actually the same to that of the random vector functional-link (RVFL) network [14], [15] where the hidden neurons are randomly selected and only the weights of the output layer need to be trained. Hence, ELM can be regarded as the single-hidden-layer RVFL network.…”

Section: Introductionmentioning

confidence: 99%

Extreme Maximum Margin Clustering

Zhang

Xia

Liu

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYMaximum margin clustering (MMC) is a newly proposed clustering method that extends the large-margin computation of support vector machine (SVM) to unsupervised learning. Traditionally, MMC is formulated as a nonconvex integer programming problem which makes it difficult to solve. Several methods rely on reformulating and relaxing the nonconvex optimization problem as semidefinite programming (SDP) or second-order cone program (SOCP), which are computationally expensive and have difficulty handling large-scale data sets. In linear cases, by making use of the constrained concave-convex procedure (CCCP) and cutting plane algorithm, several MMC methods take linear time to converge to a local optimum, but in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maximum margin clustering (EMMC) algorithm based on ELM. It can perform well in nonlinear cases. Moreover, the kernel parameters of EMMC need not be tuned by means of random feature mappings. Experimental results on several real-world data sets show that EMMC performs better than traditional MMC methods, especially in handling large-scale data sets. key words: maximum margin clustering, unsupervised learning, extreme learning machine (ELM), random feature mapping

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Learning and generalization characteristics of the random vector functional-link net

Cited by 934 publications

References 7 publications

Intelligent Pedestrian Detection using Optical Flow and HOG

Intelligent Pedestrian Detection using Optical Flow and HOG

Classifying BCI signals from novice users with extreme learning machine

Extreme Maximum Margin Clustering

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