Article
Computer Science, Artificial Intelligence
Dejun Xu, Min Jiang, Weizhen Hu, Shaozi Li, Renhu Pan, Gary G. Yen
Summary: In this article, an incremental support vector machine (ISVM)-based dynamic multiobjective evolutionary algorithm is proposed to address real-world multiobjective optimization problems with changing objectives. The algorithm can learn online and utilize information from all historical optimal solutions, effectively tackling environmental changes.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Junyou Ye, Zhixia Yang, Mengping Ma, Yulan Wang, Xiaomei Yang
Summary: In this paper, a new regression method called epsilon-kernel-free soft quadratic surface support vector regression (epsilon-SQSSVR) is proposed. The method converts the regression problem into a classification problem and constructs an optimization problem based on maximizing the sum of relative geometrical margin of each training point. The model is nonlinear, kernel-free, and highly interpretable.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Honorius Galmeanu, Razvan Andonie
Summary: This paper studies the problem of learning data sample distribution with concept drift. The author introduces a Weighted Incremental-Decremental SVM (WIDSVM) approach that can handle concept drift data streams with shifting windows, and it achieves significantly higher classification accuracy compared to traditional SVM methods.
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
Automation & Control Systems
Hang Yu, Jie Lu, Guangquan Zhang
Summary: This paper introduces Continuous Support Vector Regression (C-SVR) for addressing nonstationary streaming data regression problems. C-SVR continuously learns input-output functions over a series of time windows and connects them through an additional similarity term in order to achieve better performance in handling concept drift.
IEEE TRANSACTIONS ON CYBERNETICS
(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.
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
Alfredo Marin, Luisa I. Martinez-Merino, Justo Puerto, Antonio M. Rodriguez-Chia
Summary: This paper introduces an exact method for a cost sensitive extension of the standard SVM, which outperforms classical models and previous heuristic solutions, especially when utilizing nonlinear kernel functions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Ahmad Mousavi, Zheming Gao, Lanshan Han, Alvin Lim
Summary: We propose L1 norm regularized quadratic surface support vector machine models for binary classification in supervised learning. Theoretical analysis and experiments demonstrate the advantages of this model, including the existence and uniqueness of the optimal solution, consistency with standard SVMs on linearly separable data sets, detection of true sparsity pattern on quadratically separable data sets when the L1 norm penalty parameter is large enough, and promising practical efficiency.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2022)
Article
Automation & Control Systems
Ning Chu, Weimin Kang, Xinhua Yao, Jianzhong Fu
Summary: This paper proposes an online prediction method for the roundness of grinding workpieces based on vibration signals. Vibration sensors are used to collect vibration signals during grinding, and wavelet packet denoising is used to preprocess original signals to obtain effective vibration signals. Then use time domain analysis and frequency domain analysis to extract features and normalize them to form feature vectors. The roundness of the finished workpiece is measured using a shape-measuring instrument and integrated with the feature vectors to generate a usable data set. The support vector machine (SVM) algorithm is implemented using A Library for Support Vector Machines (LIBSVM), and a prediction model is constructed. Use the data set to train the model and evaluate the accuracy of the model to verify the effectiveness of the model. The results show that the prediction accuracy of the prediction method can reach 92.86%, and it can better predict whether the roundness is qualified.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Honorius Galmeanu, Razvan Andonie
Summary: This study introduces a model called AIDSVM, which adjusts the width of the sliding window using the Hoeffding statistical test to adapt to concept drift. Experimental results show a significant improvement in accuracy when encountering concept drift, compared to similar drift detection models defined in the literature. AIDSVM is efficient, as it does not require retraining from scratch after the sliding window slides.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
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
Computer Science, Artificial Intelligence
Sambhav Jain, Reshma Rastogi
Summary: This paper proposes Parametric non-parallel support vector machines for binary pattern classification. The model brings noise resilience and sparsity by intelligently redesigning the Support vector machine optimization. The experimental results validate its scalability for large scale problems.
Article
Dentistry, Oral Surgery & Medicine
Asterios Christodoulou, Georgios Mikrogeorgis, Triantafillia Vouzara, Konstantinos Papachristou, Christos Angelopoulos, Nikolaos Nikolaidis, Ioannis Pitas, Kleoniki Lyroudia
ACTA ODONTOLOGICA SCANDINAVICA
(2018)
Article
Engineering, Electrical & Electronic
Olga Zoidi, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2018)
Article
Computer Science, Information Systems
Ioannis Kapsouras, Nikos Nikolaidis
MULTIMEDIA TOOLS AND APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Nikolaos Tsapanos, Anastasios Tefas, Nikolaos Nikolaidis, Ioannis Pitas
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Engineering, Electrical & Electronic
Ioannis Mademlis, Nikos Nikolaidis, Anastasios Tefas, Ioannis Pitas, Tilman Wagner, Alberto Messina
IEEE SIGNAL PROCESSING MAGAZINE
(2019)
Editorial Material
Engineering, Electrical & Electronic
Nikos Nikolaidis, Nikolaos V. Boulgouris, Lisimachos Paul Kondi, Christophoros Nikou, Konstantinos N. Plataniotis
IEEE SIGNAL PROCESSING MAGAZINE
(2019)
Article
Computer Science, Theory & Methods
Ioannis Mademlis, Nikos Nikolaidis, Anastasios Tefas, Ioannis Pitas, Tilman Wagner, Alberto Messina
ACM COMPUTING SURVEYS
(2019)
Article
Engineering, Electrical & Electronic
Ioannis Mademlis, Vasileios Mygdalis, Nikos Nikolaidis, Maurizio Montagnuolo, Fulvio Negro, Alberto Messina, Ioannis Pitas
IEEE TRANSACTIONS ON BROADCASTING
(2019)
Article
Computer Science, Information Systems
Lason Karakostas, Loannis Madernlis, Nikos Nikolaidis, Loannis Pitas
INFORMATION SCIENCES
(2020)
Article
Computer Science, Information Systems
Ioannis Mademlis, Arturo Torres-Gonzalez, Jesus Capitan, Maurizio Montagnuolo, Alberto Messina, Fulvio Negro, Cedric Le Barz, Tiago Goncalves, Rita Cunha, Bruno Guerreiro, Fan Zhang, Stephen Boyle, Gregoire Guerout, Anastasios Tefas, Nikos Nikolaidis, David Bull, Ioannis Pitas
Summary: This paper presents a novel multiple-UAV software/hardware architecture for media production in outdoor settings. By enhancing UAV cognitive autonomy and integrating multiple UAVs, advantages of easier cinematography planning and safer execution of the plan can be achieved.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Charalampos Symeonidis, Ioannis Mademlis, Ioannis Pitas, Nikos Nikolaidis
Summary: Recent development in artificial intelligence, control, and sensing technologies have contributed to the advancement of autonomous UAVs. The paper introduces a new annotated video dataset called AUTH-Persons, containing real and synthetic footage for training and evaluating aerial-view person detection algorithms. This dataset is utilized to assess the generalization performance of state-of-the-art detection frameworks and compare various NMS algorithms in crowded scenes.
2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP
(2022)
Article
Computer Science, Theory & Methods
Efstratios Kakaletsis, Charalampos Symeonidis, Maria Tzelepi, Ioannis Mademlis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Summary: Flight safety is a crucial issue in UAV navigation, especially when it comes to autonomous drones and UAV swarms. Although the main aspects of autonomous UAV technologies are well-covered, ensuring safe flying in unstructured environments, such as avoiding crowds and emergency landing, are often overlooked. This overview focuses on the importance of computer vision in addressing these safety issues and introduces a computer vision-based UAV flight safety pipeline.
ACM COMPUTING SURVEYS
(2022)
Proceedings Paper
Computer Science, Software Engineering
Efstratios Kakaletsis, Olga Zoidi, Ioannis Tsingalis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Ioannis Kapsouras, Nikos Nikolaidis
2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
(2018)
Proceedings Paper
Computer Science, Software Engineering
Iason Karakostas, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2018)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)