Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Yuan-hai SHAO"'
Autor:
Meng-Xian Zhu, Yuan-Hai Shao
Publikováno v:
IEEE Access, Vol 11, Pp 41142-41157 (2023)
In this paper, we study the classification problem by estimating the conditional probability function of the given data. Different from the traditional expected risk estimation theory on empirical data, we calculate the probability via Fredholm equat
Externí odkaz:
https://doaj.org/article/9aef5412ed2443de8749da0c37f2071e
Publikováno v:
IEEE Access, Vol 11, Pp 34250-34259 (2023)
Recently, an absolute value inequalities discriminant analysis criterion with robustness and sparseness for supervised dimensionality reduction was studied. However, it obtains discriminant directions one by one through greedy search, which makes the
Externí odkaz:
https://doaj.org/article/9d8d8fa0e6034d3eb0449d93bd2ccfdb
Autor:
Zhao-qun LI, Ting-ting YUAN, Shao-wei CUI, Ying-jie ZHAO, Yuan-hai SHAO, Jian-nong SHANG, Zong-xiu LUO, Xiao-ming CAI, Lei BIAN, Zong-mao CHEN
Publikováno v:
Journal of Integrative Agriculture, Vol 22, Iss 1, Pp 195-201 (2023)
The tea tussock moth (Euproctis pseudoconspersa) is one of the most destructive chewing pests in tea plantations and causes a serious allergic reaction on the skin of tea plantation workers. The sex pheromone components of its Japanese population wer
Externí odkaz:
https://doaj.org/article/7e3ff43a192449a888075080b11243b1
Publikováno v:
IEEE Access, Vol 9, Pp 24499-24512 (2021)
In this work, we propose an unsupervised multiple parametric-margin support vector clustering (MPMSVC) for noisy clustering tasks. The main idea of MPMSVC is to find a parametric-margin center hyperplane for each cluster in a manner that gathers the
Externí odkaz:
https://doaj.org/article/3f21340b235544d09306fd4e476fad5e
Autor:
Jun Zhang, Yuan-Hai Shao, Ling-Wei Huang, Jia-Ying Teng, Yu-Ting Zhao, Zhu-Kai Yang, Xin-Yang Li
Publikováno v:
IEEE Access, Vol 8, Pp 2188-2199 (2020)
Stock index price forecasting is a consistent focus of business intelligence. Various factors influence stock index price forecasting, such as technical indicators, financial news, business status, and the macroeconomics situation. In addition, many
Externí odkaz:
https://doaj.org/article/c38c944963964a55ba4a878c944d9cf4
Publikováno v:
IEEE Access, Vol 7, Pp 47171-47184 (2019)
Generalized eigenvalue proximal support vector machine (GEPSVM) and its improvement IGEPSVM are excellent nonparallel classification methods due to their excellent generalization. However, all of them adopt the square L2-norm metric to implement thei
Externí odkaz:
https://doaj.org/article/ffb4d8e1b88943a0a37cc9bb1f89b361
Publikováno v:
IEEE Access, Vol 7, Pp 65390-65404 (2019)
Support vector machine (SVM) and twin SVM (TWSVM) are sensitive to the noisy classification, due to the unlimited measures in their losses, especially for imbalanced classification problem. In this paper, by combining the advantages of the correntrop
Externí odkaz:
https://doaj.org/article/86de4e2e4b4e4d989697d716ef97f914
Publikováno v:
IEEE Access, Vol 6, Pp 20334-20347 (2018)
As a useful classification method, generalized eigenvalue proximal support vector machine (GEPSVM) is recently studied extensively. However, it may suffer from the sensitivity to outliers, since the L2-norm is used as a measure distance. In this pape
Externí odkaz:
https://doaj.org/article/d17819732b264d0d8bad0f8063b74766
Publikováno v:
IEEE Access, Vol 6, Pp 72551-72562 (2018)
Principal component analysis (PCA) and linear discriminant analysis (LDA) have been extended to be a group of classical methods in dimensionality reduction for unsupervised and supervised learning, respectively. However, compared with the PCA, the LD
Externí odkaz:
https://doaj.org/article/2d00a1bc22dc4f0eac24ddc842c25b30
Publikováno v:
Information, Vol 12, Iss 12, p 515 (2021)
The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore,
Externí odkaz:
https://doaj.org/article/91bb9f7270fa47229f6a253f1200a254