模糊逻辑真的能改善简单的机器学习算法吗？
我正在阅读有关模糊逻辑的内容，但我只是不明白它如何在大多数情况下改进机器学习算法（它似乎相对经常应用）。
以 k 个最近邻为例。如果你有一堆属性，比如颜色：[红，蓝，绿，橙]，温度：[实数]，形状：[圆形，方形，三角形]，你不能除了真正的编号属性之外，确实模糊了其中的任何一个（如果我错了，请纠正我），而且我不知道这除了将东西放在一起之外还能有什么改进。
如何使用机器模糊逻辑来改进机器学习？大多数时候，您在大多数网站上找到的玩具示例似乎并不那么适用。
I'm reading about fuzzy logic and I just don't see how it would possibly improve machine learning algorithms in most instances (which it seems to be applied to relatively often).
Take for example, k nearest neighbors. If you have a bunch a bunch of attributes like color: [red,blue,green,orange], temperature: [real number], shape: [round, square, triangle], you can't really fuzzify any of these except for the real numbered attribute (please correct me if I'm wrong), and I don't see how this can improve anything more than bucketing things together.
How can machine fuzzy logic be used to improve machine learning? The toy examples you'll find on most websites don't seem to be all that applicable, most of the time.
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当变量具有自然形状解释时，建议使用模糊逻辑。例如，[很少，很少，很多，很多]对值有一个很好的重叠梯形解释。
颜色等变量可能不会。模糊变量表示隶属度，此时它们就变得有用了。
关于机器学习，这取决于您想要应用模糊逻辑的算法的哪个阶段。在我看来，在找到簇之后（使用传统的学习技术）来确定每个簇上搜索空间中某个点的隶属度会更好，但这并不能提高每次看到的学习效果，而是可以提高分类后的效果。学习。
Fuzzy logic is advisable when the variables have a natural shape interpretation. For example, [very few, few, many, very many] have a nice overlapping trapezoid interpretation of values.
Variables like color might not. Fuzzy variables denote degree of membership, that's when they become useful.
Regarding machine learning, it depends on what stage of the algorithm you want to apply fuzzy logic. It would be better applied in my opinion after the clusters are found (using traditional learning techniques) to determining the degree of membership of a certain point in the search space on each cluster, but that doesn't improve learning per see, but classification after learning.
[圆形、方形、三角形]大多是理想类别，主要存在于几何中（即理论上）。在现实世界中，某些形状可能几乎是正方形或或多或少是圆形（圆形）。红色有许多细微差别，有些颜色与其他颜色更接近（例如，请一位女士解释绿松石色）。因此，抽象类别和一些特定值也可以作为参考，在现实世界中，对象或值不一定等于这些。
模糊隶属度允许您测量某些特定对象与理想对象的距离。使用这种度量可以让人们避免“不，它不是圆形”（这可能会导致信息丢失）并利用给定对象是（不是）圆形的度量。
[round, square, triangle] are mostly ideal categories, which exist primarily in geometry (i.e. in theory). In real world, some shapes might be almost square or more or less round (circular shape). There are many nuances of red, and some colors are closer to some others (ask a woman to explain turquoise, for example). Hence, also abstract categories and some specific values are useful as references, in real world the objects or values are not necessarily equals to these ones.
Fuzzy membership allow you to measure how far are some specific objects from some ideal. Using this measure lets one to avoid "no, it's not circular" (which might lead to information loss) and make use of the measure the given object is (not) circular.
在我看来，模糊逻辑并不是一种实际上可行的方法，除非您正在构建一个专门构建的模糊控制器或一些基于规则的结构（例如合规性/策略）。虽然模糊意味着处理 0 和 1 之间（包括 0 和 1）之间的所有内容。但是，我发现当您处理更复杂的问题（需要在 3 维空间中应用模糊逻辑方面）时，它有点缺陷。您仍然可以处理多元变量，而无需查看模糊逻辑。不幸的是，对于研究过模糊逻辑的我来说，我发现自己不同意在大维空间中的模糊集合中所采用的原理，这似乎不可行、不切实际，而且逻辑上不太合理。您将在模糊集解决方案中应用的自然语言基础也将是非常特别的，确切地说是[非常，很少，很多]，这就是您在应用程序中定义的所有内容。
在很多机器学习方面，您会发现您甚至不必在模型中构建自然语言基础。事实上，您会发现无需将模糊逻辑应用到模型的任何方面即可获得更好的结果。
In my view, fuzzy logic is not a practically viable approach to anything unless you are building a purpose build fuzzified controller or some rule based structure like for compliance/policies. Although, fuzzy implies dealing with everything between and including 0 and 1. It, however, I find is a bit flawed when you approach more complicated problems where you need to apply fuzzy logic aspects in 3 dimensional spaces. You can still approach multivariate without having to look at fuzzy logic. Unfortunately, for me having studied fuzzy logic I found myself disagreeing with the principles approached in fuzzy sets in large dimensional spaces it seems infeasible, unpractical, and not very logically sound. The natural language base that you would be applying in your fuzzy set solution will also be very adhoc what exactly is [very,few, many] this is all what you define in your application.
Alot, of machine learning aspects you will find that you don't even have to go so far as to build natural language underpinnings into your model. In fact, you will find you can achieve even better results without having to apply fuzzy logic into any aspect of your model.
强行添加模糊性会让你有点恼火。如果不是“形状”属性，而是“边数”属性，该属性将进一步分为“少”、“中”、“多”和“不可数”< /em>。考虑到适当的隶属函数，该正方形可能是“较小”和“中等”的一部分。如果您有“红色”属性来代替“颜色”属性，则可以使用 RGB 代码来创建隶属函数。正如我在数据挖掘方面的经验所说，每种方法都可以应用于每个数据集，有效的方法就是有效的。
just too irritate you a bit by forcibly adding fuzziness to this. if instead of the "shape" attribute you had a "number of sides" attribute which would have been further divided into "less", "medium", "many" and "uncountable". the square could have been a part of "less" and "medium" both given the appropriate membership function. in place of the "color" attribute, if you had "red" attribute, then using the RGB code, a membership function could have been made. so as my experience in data mining says, every method can be applied to every dataset, what works, works.
难道不能将离散集合转换为连续集合并获得与模糊性相同的效果，同时能够使用概率论的所有技术吗？
例如 size ['小', '中', '大'] ==> [0,1]
Couldn't one just convert discrete sets into continuous ones and get the same effects as fuzziness, while being able to use all the techniques of probability theory?
For instance size ['small', 'medium', 'big'] ==> [0,1]
我不清楚你在给出的示例中想要完成什么（形状、颜色等）。模糊逻辑已成功地应用于机器学习，但我个人认为它在构建策略时可能更有用。我不再继续讨论这个问题，而是向您推荐我在《PC AI》杂志 2002 年 3 月/4 月号上发表的一篇文章，希望这篇文章能够阐明这个想法：
让模糊逻辑发挥作用：模糊规则简介
It's not clear to me what you're trying to accomplish in the example you give (shapes, colors, etc.). Fuzzy logic has been used successfully with machine learning, but personally I think it is probably more often useful in constructing policies. Rather than go on about it, I refer you to an article I published in the Mar/Apr-2002 issue of "PC AI" magazine, which hopefully makes the idea clear:
Putting Fuzzy Logic to Work: An Introduction to Fuzzy Rules