Clustering by twin support vector machine and least square twin support vector classifier with uniform output coding Author links open overlay panel Lan Bai a Yuan Hai Shao b Zhen Wang a Chun Na Li c Show more Add to Mendeley This work is supported by the National Natural Science Foundation of China Nos 11501310 61703370 61866010
In this paper a new quadratic kernel free least square twin support vector machine QLSTSVM is proposed for binary classification problems The advantage of QLSTSVM is that there is no need to select the kernel function and related parameters for nonlinear classification problems After using consensus technique we adopt alternating direction
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
An improved twin support vector machine termed as fixed point twin support vectors machine is proposed in which each dual QPP in the traditional TWSVM and its variants in high dimensional space is converted into a sequence of successive minimization problems of unimodal functions in one dimension which can be solved by using line search methods like
The baseline classifier is a twin SVM implementation based on second order cone programming which confers robustness to the approach and leads to potentially better predictive performance
To address the issue we propose Twin Auxiliary Classifiers Generative Adversarial Net TAC GAN that further benefits from a new player that interacts with other players the generator and the discriminator in GAN Theoretically we demonstrate that TAC GAN can effectively minimize the divergence between the generated and real data
Visualize the biased reconstruction of AC GAN and our TAC GAN correction to this as well as Projection GAN ├── TAC BigGAN ├── scripts ├── Script to run TAC GAN on cifar100 ├── Script to run TAC GAN on
Figure 1 shows the geometric illustration of SVM classifier Over the past decade researchers have proposed new classifiers based on the SVM Nayak Naik & Behera 2015 Of these extensions of SVM the twin support vector machine TwinSVM Jayadeva Khemchandani & Chandra 2007 has received more attention
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PDF On Mar 31 2019 Amir Mir and others published LightTwinSVM A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier Find read and cite all the research you
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The design scheme for a digital twin of the pneumatic classifier is proposed which is associated with the physical entity The key technologies such as visualization design and data acquisition are analysed in order to realize the digital management of the pneumatic classifier and operating process and monitor flow field characteristics and particle motion
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
In this paper a novel binary classifier termed as GPTSVM projection twin support vector machine via Geometric Interpretation is presented In the spirit of original PTSVM GPTSVM tries to seek two projection axes one for each class such that the projected samples of one class are well separated from that of the other class along its own projection axis A pair of
The ultrafine classifier TTD is second to none when it comes to classifying soft to medium hard minerals at high throughputs for example in the production of fillers The patented classifying wheel geometry of the Turbo Twin permits high throughput rates and loading factors at high fineness values of up to d 97 = μm Due to the low
Twin Classifier Essentially the twin network can distinguish whether the two inputs are of the same type but it cannot directly classify them in multi classification tasks So we design a twin classifier in this paper After pairing data we put the input to be classified into the twin network and the twin classifier to get the predicted
Twin support vector machines TWSVMs are used as classifiers for solving problems involving unbalanced and insufficient samples Restricted Boltzmann machines RBMs are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis
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 problems but also it is four times faster in training a classifier than classical support vector machines
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
A novel twin center intuitionistic fuzzy large margin classifier with unified pinball loss for improving the performance of E noses system China No cstc2021jcyj msxmX0824 the Youth project of science and technology research program of Chongqing Education Commission of China
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
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