Article
Computer Science, Information Systems
Yangyang Shu, Qian Li, Chang Xu, Shaowu Liu, Guandong Xu
Summary: This paper proposes a unified framework to address the asymmetric distribution of information between training and testing phases in regression tasks. By integrating continuous, ordinal, and binary privileged information into the learning process of support vector regression, the proposed method outperforms the classic learning paradigm in solving practical problems.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Operations Research & Management Science
Veronica Piccialli, Marco Sciandrone
Summary: This paper presents the importance of support vector machine in machine learning and focuses on the application of nonlinear optimization in SVM. The paper analyzes the optimization methods for SVM training problems and discusses the design of efficient algorithms.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Information Systems
Hao Jiang, Dong Shen, Wai-Ki Ching, Yushan Qiu
Summary: This study proposes a high-order L4Lp (p >= 3) norm-product regularized multiple kernel learning framework to optimize the parameter and performance of kernel functions, while avoiding the difficulty of parameter specification through optimizing linear combinations. Experimental results demonstrate the effectiveness of the proposed approach on several benchmark datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Haiyan Chen, Ying Yu, Yizhen Jia, Bin Gu
Summary: This paper proposes an incremental learning algorithm ILTSVM based on the path following technique under the framework of infinitesimal annealing for training TSVM in handling large-scale data. Experimental results show that the proposed algorithm is the most effective and fastest method for training TSVM.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Wojciech Dudzik, Jakub Nalepa, Michal Kawulok
Summary: This paper addresses the optimization problem of SVMs for binary classification of difficult datasets, introducing an evolutionary technique and a co-evolutionary scheme. Experimental results show that the proposed algorithm outperforms popular supervised learners and other techniques for optimizing SVMs.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zhou Zhai, Heng Huang, Bin Gu
Summary: In this study, we propose a kernel path algorithm (KPSVM)-V-3 that can track the solutions of the nonconvex (SVM)-V-3 with respect to a kernel parameter. The algorithm estimates the position of the breakpoint by monitoring the change of the sample sets and uses an incremental and decremental learning algorithm to handle violating samples. Experimental results validate the effectiveness of the algorithm and demonstrate the advantage of choosing optimal kernel parameters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
M. Tanveer, Tarun Gupta, Miten Shah
Summary: This article introduces a new clustering algorithm pinTSVC to address the issues of noise sensitivity and re-sampling instability, by incorporating the pinball loss function for enhanced stability and performance in noise-corrupted datasets.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Article
Neurosciences
Hongmei Wang, Renhuan Yao, Xiaoyan Zhang, Chao Chen, Jia Wu, Minghao Dong, Chenwang Jin
Summary: This study recruited 22 radiology interns and 22 matched healthy controls to investigate how visual experience modulates resting-state brain network dynamics. The results showed significant differences in brain regions associated with visual processing, decision making, memory, attention control, and working memory between the radiology interns and control group. Using a machine learning algorithm, they achieved a classification accuracy of 88.64%. These findings provide new insights into the neural mechanisms of visual expertise.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Telecommunications
Wanman Li, Xiaozhang Liu, Anli Yan, Jie Yang
Summary: This paper introduces the evasion attack against SVM classification in the field of adversarial machine learning and proposes an effective defense strategy by optimizing the SVM kernel to enhance the robustness of the classifier.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Telecommunications
Wanman Li, Xiaozhang Liu, Anli Yan, Jie Yang
Summary: This paper discusses evasion attacks against SVM classification in adversarial machine learning, proposing a defense strategy using vulnerability function and kernel optimization. The defense method proves to be effective on benchmark datasets, improving the robustness of SVM classifiers.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Biophysics
Lizheng Pan, Ziqin Tang, Shunchao Wang, Aiguo Song
Summary: This study proposes a hierarchical feature optimization method based on peripheral physiological signals to effectively represent emotional states. The experimental results show that the proposed method achieves competitive performance in multiple types of emotion identification and has higher accuracy compared to existing techniques.
PHYSIOLOGICAL MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Jinseong Park, Yujin Choi, Junyoung Byun, Jaewook Lee, Saerom Park
Summary: In this paper, a multi-class classification method using kernel supports and a dynamical system under differential privacy is proposed. For small datasets, kernel methods, such as kernel support vector machines (SVMs), show good generalization performance with high-dimensional feature mapping. However, kernel SVMs have a fundamental weakness in achieving differential privacy because they construct decision functions based on a subset of the training data called support vectors. To address these limitations, a two-phase classification algorithm based on support vector data description (SVDD) is developed. It generates a differentially private SVDD (DP-SVDD) by perturbing the sphere center in a high-dimensional feature space and partitions the input space using a dynamical system for classification.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Gherardo Varando, Salvador Catsis, Emiliano Diaz, Gustau Camps-Valls
Summary: Bivariate causal discovery is the task of inferring the causal relationship between two random variables from observational data. This paper proposes an ensemble algorithm that combines classical and data-driven methods, achieving superior performance on various synthetic and real-world problems.
APPLIED SOFT COMPUTING
(2024)