Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 2,607 papers for “multi-class support vector machine”

A Fast Multi-Class Support Vector Machine

Jian Li · 2007

A binary encoding based fast multi-class support vector machine(SVM)is introduced.How to avoid the uneven class size of each SVM in the multi-classification system is discussed based on the encoding method.Then the strategy of searching the optimal division of different classes is proposed.Thus,with little loss of accuracy the system has a higher classification speed than the traditional ones.Therefore,the classifier is suitable for real time or online systems.Finally,the introduced classification system is evaluated by experiments.

Cited by 4

Multi-Class L2,1-Norm Support Vector Machine

Xiao Cai, Feiping Nie, Heng Huang et al. · 2011

Feature selection is an essential component of data mining. In many data analysis tasks where the number of data point is much less than the number of features, efficient feature selection approaches are desired to extract meaningful features and to eliminate redundant ones. In the previous study, many data mining techniques have been applied to tackle the above challenging problem. In this paper, we propose a new ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm SVM, that is, multi-class hinge loss with a structured regularization term for all the classes to naturally select features for multi-class without bothering further heuristic strategy. Rather than directly solving the multi-class hinge loss with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm regularization minimization, which has not been solved before due to its optimization difficulty, we are the first to give an efficient algorithm bridging the new problem with a previous solvable optimization problem to do multi-class feature selection. A global convergence proof for our method is also presented. Via the proposed efficient algorithm, we select features across multiple classes with jointly sparsity, i.e., each feature has either small or large score over all classes. Comprehensive experiments have been performed on six bioinformatics data sets to show that our method can obtain better or competitive performance compared with exiting state-of-art multi-class feature selection approaches.

Cited by 71

Structural Fuzzy Multi-class Support Vector Machine

Kai Li, Jie Li · Journal of Physics Conference Series · 2020

Abstract Support vector machine is a kind of generalized linear classifier that classifies data in a supervised learning manner, and its decision boundary is the maximum margin hyperplane for solving learning samples. It is used to solve the problem of binary classification. For multi-classification problems, a combination method of multiple binary classifiers is usually used to solve, but such methods are prone to generate inseparable regions of data. Therefore, on the basis of constructing a multi-class problem directly, using the pinball loss function, and introducing the structural information of different classes in the data and the role of different samples, a new support vector machine algorithm Pin-SFSimMSVM for solving multi-classification problems is proposed. It not only retains the advantages of avoiding the existence of inseparable regions and fast calculation speed of multiple types of data, but also is insensitive to noise and resampling data, and has greatly improved the accuracy. The effectiveness of the proposed algorithm is verified by experimenting on UCI standard data sets and comparing with some multi-classification algorithms.

Cited by 1Open access

Multi-Class Support Vector Machine with Differential Privacy

J.-G. Park, Yujin Choi, Jaewook Lee · ArXiv.org · 2025

With the increasing need to safeguard data privacy in machine learning models, differential privacy (DP) is one of the major frameworks to build privacy-preserving models. Support Vector Machines (SVMs) are widely used traditional machine learning models due to their robust margin guarantees and strong empirical performance in binary classification. However, applying DP to multi-class SVMs is inadequate, as the standard one-versus-rest (OvR) and one-versus-one (OvO) approaches repeatedly query each data sample when building multiple binary classifiers, thus consuming the privacy budget proportionally to the number of classes. To overcome this limitation, we explore all-in-one SVM approaches for DP, which access each data sample only once to construct multi-class SVM boundaries with margin maximization properties. We propose a novel differentially Private Multi-class SVM (PMSVM) with weight and gradient perturbation methods, providing rigorous sensitivity and convergence analyses to ensure DP in all-in-one SVMs. Empirical results demonstrate that our approach surpasses existing DP-SVM methods in multi-class scenarios.

Cited by 0Open access

Multi-Class Support Vector Machine with Maximizing Minimum Margin

Feiping Nie, Zhezheng Hao, Rong Wang · Proceedings of the AAAI Conference on Artificial Intelligence · 2024

Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximizing the "margin", which represents the minimum distance between instances and the decision boundary. Although many efforts have been dedicated to expanding SVM for multi-class case through strategies such as one versus one and one versus the rest, satisfactory solutions remain to be developed. In this paper, we propose a novel method for multi-class SVM that incorporates pairwise class loss considerations and maximizes the minimum margin. Adhering to this concept, we embrace a new formulation that imparts heightened flexibility to multi-class SVM. Furthermore, the correlations between the proposed method and multiple forms of multi-class SVM are analyzed. The proposed regularizer, akin to the concept of "margin", can serve as a seamless enhancement over the softmax in deep learning, providing guidance for network parameter learning. Empirical evaluations demonstrate the effectiveness and superiority of our proposed method over existing multi-classification methods. Complete version is available at https://arxiv.org/pdf/2312.06578.pdf. Code is available at https://github.com/zz-haooo/M3SVM.

Cited by 14Open access

A New Support Vector Machine for Multi-class Classification

Naiyang Deng, Zhiquan Qi, Yingjie Tian · 2005

Support vector machines (SVMs) for classification - in short SVC - have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in algorithm K-SVCR and algorithm nu-K-SVCR, a linear programming (LP) problem is introduced in our algorithm. This leads to a new algorithm for multi-class problem, K-class linear programming support vector classification-regression (K-LSVCR). Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is almost as efficient as K-SVCR and nu-K-SVCR, while considerably faster than them

Cited by 17

Nonparallel Hyperplanes Support Vector Machine for Multi-class Classification

Xuchan Ju, Yingjie Tian, Dalian Liu et al. · Procedia Computer Science · 2015

In this paper, we proposed a nonparallel hyperplanes classifier for multi-class classification, termed as NHCMC. This method inherits the idea of multiple birth support vector machine(MBSVM), that is the “max” decision criterion instead of the “min” one, but it has the incomparable advantages than MBSVM. First, the optimization problems in NHCMC can be solved efficiently by sequential minimization optimization (SMO) without needing to compute the large inverses matrices before training as SVMs usually do; Second, kernel trick can be applied directly to NHCMC, which is superior to existing MBSVM. Experimental results on lots of data sets show the efficiency of our method in multi-class classification accuracy.

Cited by 12Open access

A generic model of multi-class support vector machine

Yann Guermeur · International Journal of Intelligent Information and Database Systems · 2012

Roughly speaking, there is one main model of pattern recognition support vector machine, with several variants of lower popularity. On the contrary, among the different multi-class support vector machines which can be found in literature, none is clearly favoured. On the one hand, they exhibit distinct statistical properties. On the other hand, multiple comparative studies between multi-class support vector machines and decomposition methods have highlighted the fact that in practice, each model has its advantages and drawbacks. In this article, we introduce a generic model of multi-class support vector machine. It provides the first unifying definition of all the machines of this kind published so far. This contribution makes it possible to devise new machines meeting specific requirements as well as to analyse globally the statistical properties of the multi-class support vector machines.

Cited by 32

Solving Multi-Class Classification Problem Using Support Vector Machine

Bhushan S. Yelure, Siddheshwar V. Patil, Shobha B. Patil et al. · 2022

Many data mining and pattern recognition tasks have a multi-class classification problem at their core. To give satisfactory results in operational contexts, certain applications necessitate high-end machine learning systems. In the case of supervised learning problems, the most efficient ones, such as Boosting, are mono-class, which brings the dilemma of converting a global multi-class problem into many binary issues while still being able to deliver an answer to the original multi-class problem at the end. They work for binary classifications well but sometimes it is necessary to have multi-classification. The current work intends to solve this multi-class challenge by offering a comprehensive framework that includes multiple multi-class classifiers as well as principal component analysis for feature selection. Their performances in terms of accuracy are tested on world-class benchmark data-sets. The data sets with appropriate training and test samples are used for experimentation. It is seen that linear SVM performs well among all the classifiers.

Cited by 3

MULTI-CLASS SUPPORT VECTOR MACHINE CLASSIFICATION FOR HYPERSPECTRAL DATA

Xiuping Jia · 2013

A progressive two-class decision classifier (pTCDC) was developed for hyperspectral data mapping to achieve maximum class separations between each class pair. In this paper, pTCDC is tested further by comparing it with other possible ways of converting multiclass to two-class classification including one-against-all and one-to-one methods used in implementing the newly developed support vector machine classifier for remote sensing data. Experiments carried out using an AVIRIS data set are presented and the results demonstrate that pTCDC is more efficient than that of one-to-one structure and more reliable than one-again-all method.

Cited by 1

A sparse multi-class Least-Squares Support Vector Machine

Xiao-Lei Xia, Kang Li · 2008

The paper presents a new multi-class Least-Squares Support Vector Machine (LS-SVM) whose solution is sparse in the weight coefficient of support vectors. The solution of a binary LS-SVM Support Vector Machine (LS-SVM) is constructed from most of the training samples, which is referred to as the non-sparseness problem. Multi-class LS-SVMs, which are learnt on the basis of binary classifiers inevitably share the same problem of the slowdown of the resulting LS-SVM classification on test examples. This paper addresses this issue by presenting a variant of the binary LS-SVM, in which the spareness of the solution is greatly improved. A new sparse multi-class SVM is developed from the binary case. The training of the LS-SVM method is implemented using an adapted two-stage regression algorithm. Experiments on synthetic data show that the novel multi-class LS-SVM reduces the number of weights parameters with which the resultant optimal hyperplane is spanned, while maintaining competitive generalization capacity compared with conventional LS-SVM classifiers.

Cited by 6

An Improved Fuzzy Multi-class Twin Support Vector Machine

Hongmei Ju, Ling Jing · 2019

Multi-class twin support vector machine (MTSVM) is a multi-class classification algorithm. It has extensive application in the multi-class classification problems. In order to improve the classification performance of MTSVM, an improved fuzzy multiclass twin support vector machine (IF-MTSVM) is proposed. Then the solution to IF-MTSVM is derived. Experiments on ordinary UCI datasets and datasets with 5% noises added show that this new method has certain advantages over other fuzzy multi-class twin support vector machines.

Cited by 1