来自 | arXiv 作者 | Alexia Jolicoeur-Martineau
编译 | 机器之心 编辑 | 小舟、蛋酱
SVM 是机器学习领域的经典算法之一。如果将 SVM 推广到神经网络，会发生什么呢？
支持向量机（Support Vector Machine，SVM）是大多数 AI 从业者比较熟悉的概念。它是一种在分类与回归分析中分析数据的监督式学习模型与相关的学习算法。给定一组训练实例，每个训练实例被标记为属于两个类别中的一个或另一个，SVM 训练算法创建一个将新的实例分配给两个类别之一的模型，使其成为非概率二元线性分类器。SVM 模型将实例表示为空间中的点，这样映射就使得单独类别的实例被尽可能宽的明显的间隔分开。然后，将新的实例映射到同一空间，并基于它们落在间隔的哪一侧来预测所属类别。
-
将（1）视为「样本的间隔」； -
将（2）视为「数据集的间隔」。
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本篇文章来源于: 深度学习这件小事
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