ML-SLSTSVM: a new structural least square twin support vector machine for multi-label learning

Azad-Manjiri, Meisam; Amiri, Ali; Sedghpour, Alireza Saleh

doi:10.1007/s10044-019-00779-2

Cited by 19 publications

(5 citation statements)

References 39 publications

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“…Therefore, the recognition of musical piano notes is inseparable from the knowledge of various aspects such as signal processing. According to the different processing domains, the single-tone estimation method can be divided into four categories: time domain method, frequency domain method, time-frequency domain method, and cepstral domain method [ 6 ]. Among them, the short-time autocorrelation method, the harmonic peak method, the wavelet analysis method, and the cepstrum method have outstanding detection effects in each category.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method

Huang

2022

Computational Intelligence and Neuroscience

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Piano score recognition is one of the important research contents in the field of music information retrieval, and it plays an important role in information processing. In order to reduce the influence of vocals on the progress of piano notes and restore the harmonic information corresponding to piano notes, the article models the harmonic information and vocal information corresponding to piano notes in the frequency spectrum. We use the phase space reconstruction method to extract the nonlinear feature parameters in the note audio and use some of the parameters as the training set to construct the support vector machine (SVM) classifier and the other part as the test set to test the recognition effect. Therefore, the method of adaptive signal decomposition and SVM is introduced into the signal preprocessing link, and the corresponding recognition process is established. In order to improve the performance of the support vector machine, the article uses measurement learning method to obtain the measurement learning and uses the measurement learning to replace the Euclidean distance of the Gaussian kernel function of the support vector machine. The SVM method of adaptive signal decomposition and the SVM method of principal component analysis are introduced into the preprocessing process of the note signal, and then the preprocessed signal is reconstructed in phase space, and the corresponding recognition process is established. The method of directly reconstructing the phase space of the original signal has higher accuracy and can be applied to the note recognition of continuous music segments. The final experimental results show that, compared with the current popular piano score recognition algorithm, the recognition accuracy of the proposed piano score recognition algorithm is improved by 3.5% to 12.2%.

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Section: Introductionmentioning

confidence: 99%

Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method

Huang

2022

Computational Intelligence and Neuroscience

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“…Thus, parameter selection is a practical problem and should be investigated in the future. Moreover, the extension of the proposed EFTBSVM to multi-class [66]- [68], multi-label [69], [70] and multi-view [71], [72] classification problems are also interesting.…”

Section: Discussionmentioning

confidence: 99%

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

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Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

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“…Chen et al [23] in 2016 proposed multi-label TSVM (MLTSVM) that exploits multi-label information from instances. In 2020, Azad-Manjiri et al [4] proposed structural twin support vector machine for multi-label learning (ML-STSVM) which embeds the prior structural information of data into the optimization function of MLTSVM based on the same clustering technology of S-TSVM. This algorithm achieved better performance compared to other baseline multi-label learning algorithms.…”

Section: Fuzzy Twin Support Vector Machinesmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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ML-SLSTSVM: a new structural least square twin support vector machine for multi-label learning

Cited by 19 publications

References 39 publications

Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method

Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Comprehensive Review On Twin Support Vector Machines

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