小样本机器学习靠平滑假设，而非无限数据。
Practical machine learning is learning on small samples
- 基于数据点邻近性平滑预测，避免突变。
- 多种经典模型（如k-NN、SVM）都符合此范式。
- 适合资源有限的现实场景应用。
基于有限观测，机器学习需推断未来将成立的依赖关系。传统统计学习理论依赖无限训练样本，但现实中时间和总体均有限。本文提出：实用机器学习隐含一个假设——潜在依赖关系相对‘平滑’，即相近数据点间的反馈无突变。因此学习应选择能平滑逼近训练集的假设。本文正式提出‘实用学习范式’，包含描述学习器的术语与规则。结果显示，局部平滑、k-NN、决策树、朴素贝叶斯、分类与回归用的SVM等常用模型，均为该范式的实现。
原文摘要 · Abstract (English)
Based on limited observations, machine learning discerns a dependence which is expected to hold in the future. What makes it possible? Statistical learning theory imagines indefinitely increasing training sample to justify its approach. In reality, there is no infinite time or even infinite general population for learning. Here I argue that practical machine learning is based on an implicit assumption that underlying dependence is relatively ``smooth" : likely, there are no abrupt differences in feedback between cases with close data points. From this point of view learning shall involve selection of the hypothesis ``smoothly" approximating the training set. I formalize this as Practical learning paradigm. The paradigm includes terminology and rules for description of learners. Popular learners (local smoothing, k-NN, decision trees, Naive Bayes, SVM for classification and for regression) are shown here to be implementations of this paradigm.
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