Similarity measure

노트

1. A similarity measure takes these embeddings and returns a number measuring their similarity.
2. We will see that as data becomes more complex, creating a manual similarity measure becomes harder.
3. We’ll leave the supervised similarity measure for later and focus on the manual measure here.
4. To understand how a manual similarity measure works, let's look at our example of shoes.
5. In general, your similarity measure must directly correspond to the actual similarity.
6. Learning similarity measure for natural image retrieval with relevance feedback.
7. Similarity measure in a data mining context is a distance with dimensions representing features of the objects.
8. In this section, we introduce the proposed similarity measure.
9. But this property can be involved in the term weighting scheme rather than the similarity measure.
10. A similarity measure is a data mining or machine learning context is a distance with dimensions representing features of the objects.
11. Finally, the cosine similarity measures are applied to pattern recognition and medical diagnosis.
12. In this paper, we propose a similarity measure for data described in terms of the DL-lite ontology language.
13. To that end, we selected three other similarity measures, all of which operate on vectors with binary coordinates.
14. As can be seen, different similarity measures between interaction vectors generated a different number of network modules.
15. Various distance/similarity measures are available in the literature to compare two data distributions.
16. As the names suggest, a similarity measures how close two distributions are.
17. To overcome this problem, we suggest a new measure of similarity between graphs, based on the similarity measure of Wu and Palmer.
18. We have shown that this new measure satisfies the properties of a measure of similarities and we applied this new measure on examples.
19. In this paper, we consider some cosine similarity measures and distance measures between q‐rung orthopair fuzzy sets (q‐ROFSs).
20. First, we define a cosine similarity measure and a Euclidean distance measure of q‐ROFSs, their properties are also studied.
21. It enables the similarity measure to have a principled means by combining multiple types of edges from WordNet.
22. Similarity measures of intuitionistic fuzzy sets are used to indicate the similarity degree between intuitionistic fuzzy sets.
23. In this paper, a new similarity measure and weighted similarity measure between IFSs are proposed.
24. It proves that the proposed similarity measures satisfy the properties of the axiomatic definition for similarity measures.
25. A similarity measure is defined to compare the information carried by IFSs.

메타데이터

위키데이터

• ID : Q29017317

Spacy 패턴 목록

• [{'LOWER': 'similarity'}, {'LEMMA': 'measure'}]
• [{'LOWER': 'similarity'}, {'LEMMA': 'function'}]
• [{'LOWER': 'measure'}, {'LOWER': 'of'}, {'LEMMA': 'similarity'}]