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Computer Science, Artificial Intelligence
Ziad Akram-Ali-Hammouri, Manuel Fernandez-Delgado, Eva Cernadas, Senen Barro
Summary: The Support Vector Machine is an important machine learning algorithm that performs well on many classification problems. However, it is slow and requires a lot of memory when dealing with large datasets. To address this issue, a fast support vector classifier is proposed with efficient training, small prototypes collection, and fast kernel spread selection method.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
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Computer Science, Artificial Intelligence
Kai Ming Ting, Jonathan R. Wells, Takashi Washio
Summary: State-of-the-art large scale online kernel learning focuses on improving efficiency by limiting support vectors and using approximate feature maps, which can compromise predictive accuracy. The proposed approach, using Isolation Kernel, achieves efficient large scale online kernel learning without sacrificing accuracy.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
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Automation & Control Systems
Ruoyu Wang, Miaomiao Su, Qihua Wang
Summary: Nonparametric regression imputation is commonly used in missing data analysis, but it suffers from the curse of dimension. This paper proposes two distributed nonparametric regression imputation methods, which are evaluated through simulation studies and illustrated in a real data analysis.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
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Computer Science, Artificial Intelligence
Luiz C. B. Torres, Cristiano L. Castro, Frederico Coelho, Antonio P. Braga
Summary: This brief introduces a geometrical approach for obtaining large margin classifiers by exploring the geometrical properties of the data set through a Gabriel graph and Gaussian mixture model. Experimental results show that the proposed method is statistically equivalent to using SVMs for obtaining solutions. Furthermore, this method does not require optimization and can be extended to large data sets using the cascade SVM concept.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
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Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
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Computer Science, Artificial Intelligence
Shu-Chuan Chu, Zhongjie Zhuang, Jeng-Shyang Pan, Ali Wagdy Mohamed, Chia-Cheng Hu
Summary: This paper studies large-scale multi-objective feature selection problems and proposes an evolutionary algorithm SparseEA for solving large-scale sparse multi-objective optimization problems. ReliefF is used to calculate feature weights, which are then combined with the feature scores of SparseEA to guide the evolution process. Differential Evolution difference operators are introduced to increase solution diversity and help escape from local optima. Comparative experiments on large-scale datasets show that the proposed algorithm outperforms the original SparseEA and state-of-the-art algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
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Genetics & Heredity
Hannah Klinkhammer, Christian Staerk, Carlo Maj, Peter Michael Krawitz, Andreas Mayr
Summary: Polygenic risk scores (PRS) evaluate individual genetic liability and are important in clinical risk stratification. This study develops an efficient algorithm, snpboost, for fitting multivariable models to genetic data for improved PRS predictive performance. By iteratively working on smaller batches of variants most correlated with residuals, snpboost increases computational efficiency without sacrificing prediction accuracy. Results show competitive prediction accuracy and efficiency compared to other commonly used methods, making snpboost a valuable tool for constructing PRS.
FRONTIERS IN GENETICS
(2023)
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Computer Science, Information Systems
Yan Wu, Xianglong Liu, Haotong Qin, Ke Xia, Sheng Hu, Yuqing Ma, Meng Wang
Summary: In this paper, the multi-table learning problem for video search is studied, aiming to learn binary codes by capturing intrinsic video similarities from both visual and temporal aspects to build multiple hash table indices. Under the boosting learning framework, the binary codes, hash functions and temporal variation of each table are efficiently and jointly optimized.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
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Computer Science, Artificial Intelligence
Rongda Chen, Zhixia Yang, Junyou Ye
Summary: This article discusses the challenges of using support vector machine (SVM) models in multiview learning and proposes two multiview classifiers, C-MKNSVM and ?-MKNSVM, which overcome the difficulties by using kernel-free techniques. Experimental results show that these classifiers outperform traditional MVL classifiers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
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Computer Science, Artificial Intelligence
Tian Yang, Yanfang Deng, Bin Yu, Yuhua Qian, Jianhua Dai
Summary: This paper proposes a local feature selection method based on related family, which can accelerate data processing for large-scale data sets. The experiments demonstrate that the proposed algorithm is 405 times faster than LARD on partially labeled data sets while maintaining high classification accuracy. Additionally, this algorithm can effectively process partially labeled large-scale data sets with 5,000,000 samples or 20,000 features on a typical personal computer.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
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Engineering, Electrical & Electronic
Takayuki Nagata, Keigo Yamada, Kumi Nakai, Yuji Saito, Taku Nonomura
Summary: The randomized group-greedy (RGG) method and its customized method are proposed for large-scale sensor selection problems. The customized method involves selecting a portion of the compressed sensor candidates using a low-cost method to compensate for the deterioration in the solution. The proposed method provides better optimization results than the original method with a similar computational cost.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
L. Jeff Hong, Guangxin Jiang, Ying Zhong
Summary: This paper investigates the potential issues of developing parallel procedures to solve large-scale ranking and selection (R&S) problems. A new type of fixed-budget procedure, called the fixed-budget knockout-tournament (FBKT) procedure, is proposed and shown to outperform existing fixed-budget procedures in terms of maintaining correct selection. The procedure is also shown to be suitable for solving large-scale problems in parallel computing environments.
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(2022)
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Computer Science, Artificial Intelligence
Zhiyuan Dang, Xiang Li, Bin Gu, Cheng Deng, Heng Huang
Summary: In this paper, we propose a novel large-scale nonlinear AUC maximization method, TSAM, which combines random Fourier feature approximation and triply stochastic gradient descents. The experimental results demonstrate the scalability and computational efficiency of TSAM, while maintaining good generalization performance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
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Computer Science, Theory & Methods
Zihao Zeng, Chubo Liu, Zhuo Tang, Kenli Li, Keqin Li
Summary: AccTFM is an intra-layer model parallelization optimization strategy for Transformer models. It accelerates the training process by introducing fine-grained pipeline execution and hybrid communication compression strategies. Experimental results show that AccTFM can shorten the training time by up to 2.08x compared to state-of-the-art distributed training techniques.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
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Automation & Control Systems
Jing Chen, Yawen Mao, Min Gan, Feng Ding
Summary: An adaptive regularised kernel-based method is proposed in this study to reduce parameter estimation variances for large-scale systems. The method overcomes the limitations of traditional methods by setting the values of diagonal elements of the kernel matrix based on the inner products between the output set and information vectors, resulting in higher estimation accuracy.
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Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao
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He Ma, Ziyang Li, Mohamad Tayarani, Guoxiang Lu, Hongming Xu, Xin Yao
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Zhichen Gong, Huanhuan Chen, Bo Yuan, Xin Yao
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Ran Cheng, Mohammad Nabi Omidvar, Amir H. Gandomi, Bernhard Sendhoff, Stefan Menzel, Xin Yao
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Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)