Support Vector Machine
Support Vector Machine
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DOI：
10.1007/978-3-540-73170-2_4
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发表时间：
2007
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影响因子：
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通讯作者：
A. Ukil
中科院分区：
文献类型：
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作者：
A. Ukil
经典的回归和贝叶斯分类统计技术建立在一个严格的假设基础上，即潜在的概率分布是已知的。然而，在现实生活中，我们经常会遇到只有记录的训练模式的无分布回归或分类任务，这些训练模式是高维的，本质上是空的。支持向量机（SVM）是学习模式识别（分类）任务中的分离函数或在回归问题中执行函数估计的相对较新的和有前途的方法之一。支持向量机起源于Vapnik （Vapnik 1995）的统计学习理论（SLT），用于“从数据中无分布学习”。
The classical regression and Bayesian classification statistical techniques stand upon a strict assumption that the underlying probability distribution is known. However, in real life, oftentimes we are confronted with distribution-free regression or classification tasks with only recorded training patterns which are high-dimensional and empty in nature.Support vector machine (SVM) is one of the relatively new and promising methods for learning separating functions in pattern recognition (classification) tasks, or for performing function estimation in regression problems. SVMs were originated from the statistical learning theory (SLT) by Vapnik (Vapnik 1995) for ‘distributionfree learning from data’.