The existing multi-label support vector machine (Rank-SVM) has an extremely high computational complexity due to a large number of variables in its quadratic programming. When the Frank–Wolfe (FW) method is applied, a large-scale linear programming still needs to be solved at any iteration. Therefore it is highly desirable to design and implement a new efficient SVM-type multi-label algorithm. ...

This paper is an overview of a recent approach for solving linear support vector machines (SVMs), the PEGASOS algorithm. The algorithm is based on a technique called the stochastic subgradient descent and employs it for solving the optimization problem posed by the soft margin SVM a very popular classifier. We briefly introduce the SVM problem and one of the widely used solvers, SVM light, then...

In this paper, we first introduce some facts about semi-supervised learning and its often used methods such as generative mixture models, self-training, co-training and Transductive SVM and so on. Then we present a self-training semi-supervised SVM algorithm based on which we give out a modified algorithm. In order to demonstrate its validity and effectiveness, we carry out some experiments whi...

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FP...

The standard -norm SVM is known for its good performance in twoclass classification. In this paper, we consider the -norm SVM. We argue that the -norm SVM may have some advantage over the standard -norm SVM, especially when there are redundant noise features. We also propose an efficient algorithm that computes the whole solution path of the -norm SVM, hence facilitates adaptive selection of th...

In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses variou...

The nu-support vector machine (nu-SVM) for classification proposed by Schölkopf, Smola, Williamson, and Bartlett (2000) has the advantage of using a parameter nu on controlling the number of support vectors. In this article, we investigate the relation between nu-SVM and C-SVM in detail. We show that in general they are two different problems with the same optimal solution set. Hence, we may ex...

internet applications spreading and its high usage popularity result insignificant increasing of cyber-attacks. consequently, network security has becomea matter of importance and several methods have been developed for these attacks.for this purpose, intrusion detection systems (ids) are being used to monitor theattacks occurred on computer networks. data mining techniques, machinelearning, ne...

The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an ef£cient algorithm that computes the whole solution path of the 1-norm SVM, hence facilitates adaptive selection of...

This paper describes a new application of the Bees Algorithm to the optimisation of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. The algorithm, which is a swarm-based algorithm inspired by the food foraging behaviour of honey bees, was also employed to select the components making up the feature vectors to be presented to the SVM. The objective of the work w...