FLANN 的使用可以归入机器学习范畴吗？
我编写了一个程序来计算 SURF 特征，然后使用 FLANN（最近邻快速库）来匹配并显示最近邻。现在可以将 FLANN 的使用视为使用机器学习，因为据我了解，它是 k-最近邻搜索的近似版本，被视为机器学习算法（监督学习）。
I have written a program to compute SURF features and then use FLANN(Fast Library for Nearest Neighbour) to match and show the nearest neighbours. Now can the usage of FLANN be considered as using machine learning as it is my understanding that it is a an approximate version of k- nearest neighbour search which is considered as machine learning algorithm(supervised learning).
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您会在许多机器学习出版物中找到诸如 FLANN、LSH、Spectral Hashing 和 KD-tree（变体）之类的方法的提及。
然而，正如您所说，这些方法本身不是学习器/分类器，但它们可能经常在典型的机器学习应用程序中使用。根据您的示例，FLANN 不是监督分类器，但它可用于显着改进标记器和推荐器。
（也就是说，这个问题可能更适合 CrossValidated 或提议的 机器学习论坛。）
You will find mention of methods like FLANN, LSH, Spectral Hashing, and KD-tree (variants) in a lot of machine learning publications.
However, as you said, these methods themselves are not learners/classifiers, but they may often be used within typical machine learning applications. Per your example, FLANN is not a supervised classifier, but it can be used to significantly improve taggers and recommenders.
(That said, this question may be more appropriate for CrossValidated or the proposed Machine Learning forum.)
FLANN只是一种近似的最近邻搜索结构；那不是机器学习。
但是使用 FLANN 的 K 最近邻分类器是机器学习。
FLANN is just an approximate nearest neighbor search stucture; that's not machine learning.
But your K-nearest-neighbor classifier that uses FLANN is machine learning.