A polynomial kernel neural network classifier based on random sampling and information gain

Xiao, Yueyue; Huang, Wei; Oh, Sung‐Kwun; Zhu, Liehuang

doi:10.1007/s10489-021-02762-z

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The principle of the RANSAC algorithm is explained by a two-dimensional plane. RANSAC algorithm first randomly selects points, then samples the chosen topics, and finally fits a straight line [13] . They are figured for the RANSAC algorithm random sampling process.…”

Section: Ransac Algorithmmentioning

confidence: 99%

Error matching elimination based on a local affine algorithm

Chen

et al. 2022

J. Phys.: Conf. Ser.

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Aiming at the shortcomings of orb algorithm using the traditional RANSAC (random sampling consistency) algorithm to eliminate mismatches, such as the high number of iterations when the sample size is large, and the proportion of error points also affects the fitting accuracy of the traditional algorithm, a rough and fine method to eliminate mismatches is proposed; The threshold screening method is used to select the optimal threshold and preliminarily eliminate a large number of wrong matching pairs to achieve the effect of rough elimination; Then the sub-window method is used to calculate the local affine matching under all windows, reduce the number of error points, and increase the correctness of the fitting model of RANSAC algorithm. The experimental results are compared with the traditional algorithm, and the matching accuracy is improved by 6% and combined with orb-slam2.

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Section: Ransac Algorithmmentioning

confidence: 99%

Error matching elimination based on a local affine algorithm

Chen

et al. 2022

J. Phys.: Conf. Ser.

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“…QNN’s main application, similar to DNN’s, is to tackle categorization tasks [ 30 ]. Practical challenges, such as recognizing handwritten digits and the features of many living creatures, can be categorized as categorization scenes [ 31 , 32 ].…”

Section: Basic Conceptionmentioning

confidence: 99%

Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm

Zhao

Wang

et al. 2022

Entropy

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We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.

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