Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems.

2001年9月20日 · Support Vector Machines (SVM) have been recently developed in the framework of statistical learning theory, and have been successfully applied to a number of...

2020年9月30日 · In recent years, an enormous amount of research has been carried out on support vector machines (SVMs) and their application in several fields of scie…

This paper introduces the principle of SVM, kernel function selection and multi-class classification problem. at the same time, which expounds and summarizes the SVM algorithm. Secondly, it summarizes the application research of support vector machine from the aspects of handwritten digit recognition, text classification and image recognition.

2023年8月31日 · In this work, we establish a formal equivalence between the optimization geometry of self-attention and a hard-margin SVM problem that separates optimal input tokens from non-optimal tokens using linear constraints on the outer-products of token pairs.

17 小时之前 · In this paper, we propose a probabilistic quantum SVM training framework suitable for Coherent Ising Machines (CIMs). By formulating the SVM training problem as a QUBO model, we leverage CIMs' energy minimization capabilities and introduce a Boltzmann distribution-based probabilistic approach to better approximate optimal SVM solutions ...

In 1992 Vapnik and coworkers [1] proposed a supervised algorithm for classification that has since evolved into what are now known as Support Vector Machines (SVMs) [2]: a class of algorithms for classification, regression and other applications that represent the current state of the art in the field.

2018年4月13日 · In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end ...

2020年9月30日 · SVM algorithms have gained recognition in research and applications in several scientific and engineering areas. This paper provides a brief introduction of SVMs, describes many applications and summarizes challenges and …

This article is organized as follows. In Section 2, we describe SVM formulations supported in LIBSVM: C-support vector classification (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), ϵ-support vector re-gression (ϵ-SVR), and ν-support vector regression (ν-SVR). Section 3 then discusses