cosine similarity造句
造句與例句
手機(jī)版
- Cosine similarity is strictly the cosine of the angle between the two vectors.
- If the cosine similarity is good enough for you, then use it.
- The cosine similarity can be seen as a method of normalizing document length during comparison.
- See cosine similarity for further information.
- Cosine similarity takes into account these regards and also allow for the varying degrees of vertices.
- The term " cosine similarity " is sometimes used to refer to different definition of similarity provided below.
- To compute the relatedness of two words, one compares the vectors ( say and ) by computing the cosine similarity,
- The cosine similarity of i and j is the number of common neighbors divided by the geometric mean of their degrees.
- Cosine similarity then gives a useful measure of how similar two documents are likely to be in terms of their subject matter.
- Note that these bounds apply for any number of dimensions, and cosine similarity is most commonly used in high-dimensional positive spaces.
- It's difficult to see cosine similarity in a sentence. 用cosine similarity造句挺難的
- While the formula extends to vectors in general, it has quite different properties from cosine similarity and bears little relation other than its superficial appearance.
- If there is no similarity between features ( 1 } }, 0 } } for ), the given equation is equivalent to the conventional cosine similarity formula.
- Cosine similarity is technically undefined if one or both of the nodes has zero degree, but according to the convention we say that cosine similarity is 0 in these cases.
- Cosine similarity is technically undefined if one or both of the nodes has zero degree, but according to the convention we say that cosine similarity is 0 in these cases.
- In the case of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies ( tf-idf weights ) cannot be negative.
- Cosine similarity is a commonly used similarity measure for real-valued vectors, used in ( among other fields ) information retrieval to score the similarity of documents in the vector space model.
- One of the reasons for the popularity of cosine similarity is that it is very efficient to evaluate, especially for sparse vectors, as only the non-zero dimensions need to be considered.
- While LexRank uses cosine similarity of TF-IDF vectors, TextRank uses a very similar measure based on the number of words two sentences have in common ( normalized by the sentences'lengths ).
- However the most common use of " cosine similarity " is as defined above and the similarity and distance metrics defined below are referred to as " angular similarity " and " angular distance " respectively.
- The congruence coefficient can also be defined as the cosine of the angle between factor axes based on the same set of variables ( e . g ., tests ) obtained for two samples ( see Cosine similarity ).
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