2005年5月11日 · This is a book about learning from empirical data (i.e., examples, samples, measurements, records, patterns or observations) by applying support vector machines (SVMs) a.k.a. kernel machines. The...

“A mathematically elaborated topic of support vector machines in a book full with definitions and lemmas. It presents support vector machines (SVMs) as a successful modeling and prediction tool with different examples. This book has 12 chapters and 9 appendices that introduce also marginal applications of SVMs. …

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory.

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence.

Support Vector Machines (SVM’s) are a relatively new learning method used for binary classi cation. The basic idea is to nd a hyperplane which separates the d-dimensional data perfectly into its two classes. However, since example data is often not linearly separable, SVM’s introduce the

2008年8月12日 · This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in …

2009年4月1日 · information retrieval problems, particularly text classification. An SVM is a kind of large-margin classifier: it is a vector space based machine learning method where the goal is to find a decision boundary between two classes that is maximally far from any point in the training data (possibly discount-ing some points as outliers or noise).

2017年6月21日 · The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as …

支持向量机（Support Vector Machine，SVM）是 建立在弗拉基米尔·万普尼 克（Vladimir Vapnik）提出 的统计学习理论基础上的一 种使用广泛的机器学习方法 。 这本简明导论教程对支持 向量机及其理论基础进行了 全面的介绍。

《支持向量机：理论、算法与拓展》以分类问题（模式识别、判别分析）和回归问题为背景，介绍支持向量机的基本理论、方法和应用。 特别强调对所讨论的问题和处理方法的实质进行直观的解释和说明，因此具有很强的可读性。 为使具有一般高等数学知识的读者能够顺利阅读，书中首先介绍了最优化的基础知识。 《支持向量机：理论、算法与拓展》可作为理工类、管理学等专业的高年级本科生、研究生和教师的教材或教学参考书，也可供相关领域的科研人员和实际工作者阅读参 …