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노트

위키데이터

• ID : Q1430460

말뭉치

1. And that’s why it’s called Curse of Dimensionality.
2. Regarding the curse of dimensionality, there are two things to consider.
3. KNN is very susceptible to overfitting due to the curse of dimensionality.
4. Curse of dimensionality also describes the phenomenon where the feature space becomes increasingly sparse for an increasing number of dimensions of a fixed-size training dataset.
5. The idem curse of dimensionality may suggest that we keep our models simple, but on the other hand, if our model is too simple we run the risk of suffering from underfitting.
6. The Curse of Dimensionality is termed by mathematician R. Bellman in his book “Dynamic Programming” in 1957.
7. The curse of dimensionality basically means that the error increases with the increase in the number of features.
8. To overcome the issue of the curse of dimensionality, Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
9. The curse of dimensionality is a term introduced by Bellman to describe the problem caused by the exponential increase in volume associated with adding extra dimensions to Euclidean space (Bellman,).
10. Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data.
11. The difficulties related to training machine learning models due to high dimensional data is referred to as ‘Curse of Dimensionality’.
12. The curse of dimensionality (COD) was first described by Richard Bellman, a mathematician, in the context of approximation theory.
13. The first version of the curse of dimensionality is most easily understood.
14. This embarrassment of riches is called the ‘curse of dimensionality’1 (CoD) and manifests itself in a variety of ways.

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위키데이터

• ID : Q1430460

Spacy 패턴 목록

• [{'LOWER': 'curse'}, {'LOWER': 'of'}, {'LEMMA': 'dimensionality'}]