Twin Classifier China

One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. ... Twin Auxiliary Classifiers GAN Adv Neural Inf Process Syst. 2019 Dec;32:1328-1337. Authors Mingming Gong 1 2, Yanwu Xu 1, Chunyuan Li 3, Kun Zhang 2 ...

Twin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties …

A novel twin-center intuitionistic fuzzy large margin classifier with unified pinball loss for improving the performance of E-noses system. Authors: Junlin Chen, ... Recently, a robust and effective classifier termed twin bounded large margin distribution machine (TBLDM) was suggested. TBLDM is based on the twin bounded support vector machine ...

Support vector machine (SVM), as a supervised learning method, has different kinds of varieties with significant performance. In recent years, more research focused on nonparallel SVM, where twin SVM (TWSVM) is the typical one. In order to reduce the influence of outliers, more robust distance measurements are considered in these methods, but the discriminability of the …

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning.Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning.In this paper, we …

Visualize the biased reconstruction of AC-GAN and our TAC-GAN correction to this as well as Projection-GAN. ├── TAC-BigGAN ├── scripts ├── twin_ac_launch_r100_ema.sh - Script to run TAC-GAN on r100 ├── twin_ac_launch_BigGAN_ch64_bs256x8.sh - Script to run TAC-GAN on ...

Abstract—The twin support vector machine (TWSVM) clas- ... ball fuzzy support vector machine (GBFSVM) classifier partly alleviates the adverse effects of noise, but it relies solely on the distance between the granular-ball's center and the class center to design the granular-ball membership function. ... China; Weiping Ding is with the ...

Dive into the research topics of 'Robust Twin Bounded Support Vector Classifier With Manifold Regularization'. Together they form a unique fingerprint. Support Vector Machine Keyphrases

The Twin Auxiliary Classifiers GANs (TAC-GAN) [15] introduces a classifier as a new player to interact with the GAN's generator and discriminator in order to enforce the diversity in the generated ...

Conditional generative models enjoy significant progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of …

These units are available both in single and twin compartment construction. the operation of both single and twin compartment classifier is simple and continuous. the reciprocating classifier rake transports the grit up the inclined deck to a water spray. washed and drained grit is discharged at the upper end. the grit free liquid overflows ...

We improve the twin support vector machine (TWSVM) to be a novel nonparallel hyperplanes classifier, termed as ITSVM (improved twin support vector machine), for binary …

This work was supported in part by National Natural Science Foundation of China (No. 11471010) and Chinese Universities Scientific Fund. ... (PTSVM) into the basic framework of twin extreme learning machines (TELM) and first propose a novel binary classifier named projection twin extreme learning machines (PTELM). PTELM is to seek two ...

518060, China, and also with the Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China. (email: apple zjh@163, lai zhi hui@163, [email protected]) Heng Kong is with the Department of Breast and Thyroid Surgery, BaoAn Central Hospital of Shenzhen, BaoAn district, Shenzhen, Guangdong Province, China.

The recently proposed twin support vector clustering (TWSVC) is a powerful clustering method. However, TWSVC may encounter the singularity problem and it is time consuming in the learning stage. In this paper, we introduce some efficient techniques into TWSVC, and propose two clustering models, called twin bounded support vector clustering …

We propose a new distribution-free Bayes optimal classifier, called the twin minimax probability machine (TWMPM), which combines the benefits of both minimax probability machine (MPM) …

K. S. Xiahou South China University of Technology Verified email at scut ... Federated Learning with Feature Anchors to Align Feature and Classifier for Heterogeneous Data. T Zhou, J Zhang, DHK Tsang. IEEE Transactions on Mobile ... Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality. J Yu, A Alhilal, T ...

In order to solve this problem, this paper introduces the margin distribution measure of the distance from the sample to the hyperplane and the Unified Pinball Loss based on the TSVM, …

Twin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification proble ... Shaanxi, China | [c] School of Information Systems, Computing and Mathematics, Brunel ...

A distribution of 1D MoG having three overlapping mixture components with modes at 0, 3, 6, and 110, and standard deviations of 1, 2, and 3, respectively ...

This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM.

This paper identifies the source of the low diversity issue theoretically and proposes a practical solution to solve the problem, and proposes Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN), which can effectively minimize the divergence between the generated and real-data distributions. Conditional generative models enjoy remarkable progress over the past few years.

To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that adds a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that our TAC-GAN can effectively minimize the divergence between generated and real data distributions.

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A recent comprehensive evaluation [35] of various TWSVM variants on 90 UCI benchmarks datasets and 187 classifiers concludes that RELS-TWSVM is the best classifier among twin SVM classifiers. For ...

Hence, the corresponding classifier is sensitive to noise and outliers [6] and unstable for resampling. To solve this, numerous research have investigated convex or non-convex loss functions. ... The sparse pinball twin SVM [10], [11], fuzzy Lagrangian twin bounded SVM [12], and the general twin SVM with pinball loss [13], [14] were proposed ...

Support vector machine (SVM), as a popular target-domain-free classifier, holds significant importance in E-noses systems. Specifically, after processing E-noses drift data using feature-based methods, many studies employ SVM as the base classifier (Guo et al., 2022, Yan et al., 2022, Zhang et al., 2017).However, traditional SVM has many drawbacks, which …

Intelligent Data Analysis 17 (2013) 649–664 DOI 10.3233/IDA-130598 IOS Press 649 OP Y Extending twin support vector machine classifier for multi-category classification problems Juanying Xiea,b,∗, Kate Honec, Weixin Xied, Xinbo Gaob, Yong Shie and Xiaohui Liuc a School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi, China of Electronic …

Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine. We introduce the cost control to solve the problem of sample skew. Finally, based on the bi-boundary support vector machine, a two-step weight setting twin classifier is constructed.

This paper formulates a twin-hypersphere support vector machine (THSVM) classifier for binary recognition. Similar to the twin support vector machine (TWSVM) classifier, this THSVM determines two hyperspheres by solving two related support vector machine (SVM)-type problems, each one is smaller than the classical SVM, which makes the THSVM be more …