☆ 4.7 Article

A novel multi-task twin-hypersphere support vector machine for classification

INFORMATION SCIENCES (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Adversarial multi-task learning with inverse mapping for speech enhancement

Yuanhang Qiu et al.

Summary: In this paper, a novel adversarial multi-task learning with inverse mapping method is proposed for speech enhancement. The method focuses on enhancing the generator's capability of speech information capturing and representation learning, and achieves state-of-the-art performance in terms of speech quality and intelligibility.

APPLIED SOFT COMPUTING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Model-Protected Multi-Task Learning

Jian Liang et al.

Summary: Multi-task learning (MTL) is a method of learning multiple related tasks together, which can result in better trained models by leveraging commonalities. However, MTL poses a security risk as an adversary may acquire model information for one task through participation in the learning process of another task. This paper proposes a privacy-preserving MTL framework that perturbs the covariance matrix of the model matrix to prevent information leakage between models. Experimental results demonstrate the superiority of the proposed methods compared to existing privacy-preserving MTL methods.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A rough margin-based multi-task v-twin support vector machine for pattern classification

Ran An et al.

Summary: TSVM is suitable for STL problems, while MTL explores shared information between multiple tasks for better classification. The proposed rough MT-v-TSVM assigns different penalties to misclassified samples based on their positions, combining the advantages of rough v-TSVM and preserving the individuality of tasks.

APPLIED SOFT COMPUTING (2021)

添加到收藏夹

Article Computer Science, Information Systems

A deep multi-task representation learning method for time series classification and retrieval

Ling Chen et al.

Summary: This paper introduces a deep multi-task representation learning method for time series classification and retrieval, utilizing both supervised and unsupervised information. By sharing networks, the classification and retrieval tasks can benefit from each other, leading to improved accuracy and convergence speed.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

The ensemble of density-sensitive SVDD classifier based on maximum soft margin for imbalanced datasets

Xinmin Tao et al.

Summary: This paper proposes a novel density-sensitive SVDD classifier based on maximum soft margin for imbalanced datasets. By incorporating relative density-based penalty weights and maximum soft margin regularization term, the classifier focuses more on the minority class, thus improving classification accuracy. The AdaBoost ensemble version further enhances generalization performance and stability in dealing with imbalanced datasets.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD

Tianyu Hu et al.

Summary: This article proposes an NTL detection model based on deep learning and anomaly detection, named BSBND, utilizing BiWGAN and SVDD technologies. The alternate coordinating algorithm and interpreting algorithm are introduced to optimize the detection model and enhance fraudulent judgment accuracy.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Robust Multi-Task Learning With Flexible Manifold Constraint

Rui Zhang et al.

Summary: This paper proposes a novel robust multi-task learning model FMC-MTL, which incorporates flexible manifold constraint and robust loss function to handle polluted data. Experimental results demonstrate the remarkable superiority of this model in dealing with outliers.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

添加到收藏夹

Article Computer Science, Information Systems

Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods

Jie Sun et al.

Summary: This paper focuses on multiclass financial distress prediction using SVM and decomposition fusion methods, showing that OVO-SVM outperforms OVR-SVM and ECOC-SVM in overall performance and is preferred. Data preprocessing mechanisms can greatly enhance the model performance, while OVO-SVM is more competitive for predicting financial pseudosoundness and moderate financial distress compared to human expertise.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Information Systems

PMT-Net: Progressive Multi-Task Network for one-shot Person Re-Identification

Yulin Zhang et al.

Summary: PMT-Net is a novel model designed for person re-identification, which tackles the limited availability of labeled training samples issue in fully supervised Re-ID by iteratively optimizing the model and dynamically sampling reliable pseudo labels from unlabeled samples. Firstly, pedestrian attributes recognition is used as an auxiliary task, followed by estimating identity labels for unlabeled samples based on distance in feature space, and a semi-supervised clustering method called DRW-Clustering is designed to improve label estimation accuracy for unlabeled samples.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Information Systems

Twin support vector machines with privileged information

Zhiyong Che et al.

Summary: A new method named TWSVM-PI is proposed in this paper, which introduces privileged information into twin support vector machines and optimizes the objective function using the Lagrangian multiplier method to improve prediction performance. Extensive experiments show that the proposed method outperforms state-of-the-art methods in terms of accuracy and computational efficiency.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Information Systems

Multi-task learning for spatial events prediction from social data

Sungkwang Eom et al.

Summary: Multi-task learning is increasingly popular for improving prediction accuracy and cost savings, especially in predicting event types from social data. The proposed deep learning framework SEP integrates event attribute type prediction, attention mechanism, and location representation sharing method, effectively addressing data sparsity and incompleteness.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Residual multi-task learning for facial landmark localization and expression recognition

Boyu Chen et al.

Summary: The paper introduces a residual multi-task learning framework for predicting facial landmark localization and expression recognition simultaneously, utilizing a residual learning module to strengthen the connection between the two tasks and addressing the lack of training data with multi-task labels in multi-task learning.

PATTERN RECOGNITION (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-task ν-twin support vector machines

Benshan Mei et al.

NEURAL COMPUTING & APPLICATIONS (2020)

添加到收藏夹

Article Geochemistry & Geophysics

Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach

Xinwei Chen et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A grid-quadtree model selection method for support vector machines

Monica Beltrami et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

添加到收藏夹

Article Engineering, Civil

Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning

Kunpeng Zhang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A reduced universum twin support vector machine for class imbalance learning

B. Richhariya et al.

PATTERN RECOGNITION (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-Task Deep Dual Correlation Filters for Visual Tracking

Yuhui Zheng et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A spheres-based support vector machine for pattern classification

Xinjun Peng

NEURAL COMPUTING & APPLICATIONS (2019)

添加到收藏夹

Article Automation & Control Systems

A multi-class classifier based on support vector hyper-spheres for steel plate surface defects

Rongfen Gong et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2019)

添加到收藏夹

Review Multidisciplinary Sciences

An overview of multi-task learning

Yu Zhang et al.

NATIONAL SCIENCE REVIEW (2018)

添加到收藏夹

Review Computer Science, Information Systems

A brief review on multi-task learning

Kim-Han Thung et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2018)

添加到收藏夹

Article Computer Science, Information Systems

A twin-hyperspheres support vector machine with automatic variable weights for data classification

Xinjun Peng et al.

INFORMATION SCIENCES (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

Zhengxia Wang et al.

MEDICAL IMAGE ANALYSIS (2017)

添加到收藏夹

Article Automation & Control Systems

Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification

Yitian Xu

IEEE TRANSACTIONS ON CYBERNETICS (2017)

添加到收藏夹

Article Engineering, Biomedical

Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

Xiaofeng Zhu et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification

Yitian Xu et al.

KNOWLEDGE-BASED SYSTEMS (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multitask centroid twin support vector machines

Xijiong Xie et al.

NEUROCOMPUTING (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-task proximal support vector machine

Ya Li et al.

PATTERN RECOGNITION (2015)

添加到收藏夹

Article Computer Science, Information Systems

A twin-hypersphere support vector machine classifier and the fast learning algorithm

Xinjun Peng et al.

INFORMATION SCIENCES (2013)

添加到收藏夹

Article Computer Science, Information Systems

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

Salvador Garcia et al.

INFORMATION SCIENCES (2010)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

Mingrui Wu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2009)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Inductive transfer with context-sensitive neural networks

Daniel L. Silver et al.

MACHINE LEARNING (2008)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Twin support vector machines for pattern classification

Jayadeva et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2007)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.