Article
Computer Science, Artificial Intelligence
Xijiong Xie, Yanfeng Li, Shiliang Sun
Summary: This paper proposes two novel multi-view deep models, namely DMvTSVM based on DNN and AE network, to optimize the classification performance of each view by finding non-parallel hyperplanes and using similarity regularization and weight adjustment strategy to improve the model performance.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Zhizheng Liang, Lei Zhang
Summary: In this paper, novel twin support vector machines (TSVMs) are proposed to handle uncertain data, where each uncertain sample is modeled as a random vector with Gaussian distributions. By deriving an important theorem to simplify the models and using a quasi-Newton optimization algorithm, the optimization problem becomes tractable. Experimental results show that the proposed models outperform some existing algorithms in terms of classification performance, especially for uncertain cross-plane problems.
PATTERN RECOGNITION
(2022)
Article
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.
Review
Operations Research & Management Science
M. Tanveer, T. Rajani, R. Rastogi, Y. H. Shao, M. A. Ganaie
Summary: TWSVM and TSVR are emerging machine learning techniques for classification and regression challenges. TWSVM classifies data points using two nonparallel hyperplanes, while TSVR is based on TWSVM and solves two SVM-type problems. Although there has been progress in research on these techniques, there is limited literature on the comparison of different variants of TSVR.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
H. Moosaei, S. Ketabchi, M. Razzaghi, M. Tanveer
Summary: In this paper, two efficient approaches of twin support vector machines (TWSVM) are proposed, including reformulating the formulation by introducing different norms and presenting an efficient algorithm using the generalized Newton's method. Experimental results demonstrate that the new methods outperform baseline methods in terms of performance and computational time.
NEURAL PROCESSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Jiamin Xu, Huamin Wang, Libo Zhang, Shiping Wen
Summary: In this paper, a twin depth support vector machine (TDSVM) is proposed, which considers the influence of depth when calculating the distance. By strengthening the center and weakening the edge, a robust SVM framework is constructed, which can identify outliers in the dataset and achieve better generalization performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Cybernetics
M. Tanveer, M. Tabish, Jatin Jangir
Summary: Analyzing unlabeled data and identifying underlying clustering principles is essential in various fields. Novel unsupervised machine learning algorithms, such as TBSVC, are developed for this purpose. However, TBSVC is sensitive to noise and lacks resampling stability. A new method, pinSTBSVC, uses a pinball loss function to improve sparsity and performance in plane-based clustering algorithms. Experimental results show that pinSTBSVC outperforms existing methods, demonstrating its effectiveness in real-world datasets and applications.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Salim Rezvani, Xizhao Wang
Summary: In this paper, the authors propose a method called intuitionistic fuzzy twin support vector machines for imbalanced data (IFTSVM-ID) to address the challenges of imbalanced datasets. The method effectively handles noise and outliers, and uses a weighting strategy and margin-based technique to deal with imbalanced classes. Experimental results demonstrate that the proposed method outperforms other similar techniques.
Article
Computer Science, Artificial Intelligence
Huajuan Huang, Xiuxi Wei, Yongquan Zhou
Summary: This article reviews the recent developments in twin support vector regression (TSVR). It introduces the basic concepts and models of TSVR, summarizes the improved algorithms and applications in recent years, and analyzes the advantages and disadvantages of representative algorithms through experiments. The article also discusses the research conducted on TSVR.
Article
Operations Research & Management Science
Scindhiya Laxmi, S. K. Gupta, Sumit Kumar
Summary: This paper proposes an intuitionistic fuzzy regularized least square twin support vector machine method to improve the TSVM classifier by considering data uncertainties and the importance of patterns in classification. The results show that the proposed method outperforms existing methods in terms of accuracy, computational time, etc., and is also suitable for big datasets.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
Scindhiya Laxmi, S. K. Gupta
Summary: The intuitionistic fuzzy twin support vector machine for multi-categorization is developed in this study, which combines the concepts of structural and empirical risk. Empirical findings show that this method outperforms existing methods on various datasets and has good generalization capacity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wenguo Wu, Zhengchun Zhou, Avik Ranjan Adhikary, Bapi Dutta
Summary: Reinforcement learning is an important component of machine learning algorithms, but traditional algorithms have convergence speed and accuracy issues in small-scale discrete space environments. Recent research has proposed an improved algorithm based on support vector machines, which utilizes the advantages of twin support vector machines to enhance convergence speed and accuracy.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Yuan -Hai Shao, Xiao-Jing Lv, Ling-Wei Huang, Lan Bai
Summary: In this paper, a new twin support vector machine model (CPTWSVM) is proposed for estimating the conditional probability function in binary and multiclass classification problems. CPTWSVM implements empirical risk minimization on training data, which is difficult to achieve in traditional twin SVMs. Each subproblem of CPTWSVM measures the empirical risk and outputs the probability estimate of each class, solving the inconsistent measurement issues in twin SVMs. The optimization problem size of each subproblem in CPTWSVM is smaller than conditional probability SVM and is efficiently solved using a block decomposition algorithm.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Abolfazl Hasanzadeh Shadiani, Mahdi Aliyari Shoorehdeli
Summary: This paper proposes an online approach for twin support vector machine, which utilizes recursive relation to avoid repetitive calculation of inverse matrices, resulting in improved training efficiency and maintained accuracy. Experimental results demonstrate the effectiveness of this method.
NEURAL PROCESSING LETTERS
(2023)
Article
Physics, Multidisciplinary
Cesar Alfaro, Javier Gomez, Javier M. Moguerza, Javier Castillo, Jose I. Martinez
Summary: This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs) in wireless sensor networks, addressing challenges such as reducing energy consumption and minimizing data exchange. Results show comparable performance with limited regions required, facilitating the development of energy-efficient policies in WSN.
Article
Energy & Fuels
Stef Jacobs, Margot De Pauw, Senne Van Minnebruggen, Sara Ghane, Thomas Huybrechts, Peter Hellinckx, Ivan Verhaert
Summary: In this study, the focus is on 2-pipe networks with changing supply temperature achieved through the use of decentralised storage. By grouping high-temperature demands, the average supply temperature can be lowered, resulting in energy savings. The results show that grouping can lead to energy savings of up to 36% compared to conventional control strategies.
Proceedings Paper
Computer Science, Artificial Intelligence
Sara Ghane, Stef Jacobs, Wim Casteels, Christian Brembilla, Siegfried Mercelis, Steven Latre, Ivan Verhaert, Peter Hellinckx
Summary: Heating networks are typically controlled by a heating curve that is dependent on outdoor temperature, but innovative networks connected to low heat demand dwellings require advanced control strategies. Research has shown that reinforcement learning can lead to energy savings while meeting occupants' temperature requirements.
2021 IEEE INTERNATIONAL SMART CITIES CONFERENCE (ISC2)
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.