"확률적 알고리즘"의 두 판 사이의 차이

수학노트
둘러보기로 가기 검색하러 가기
(→‎메타데이터: 새 문단)
 
25번째 줄: 25번째 줄:
 
  <references />
 
  <references />
  
−
== 메타데이터 ==
+
==메타데이터==
−
 
 
 
===위키데이터===
 
===위키데이터===
 
* ID :  [https://www.wikidata.org/wiki/Q583461 Q583461]
 
* ID :  [https://www.wikidata.org/wiki/Q583461 Q583461]
 +
===Spacy 패턴 목록===
 +
* [{'LOWER': 'randomized'}, {'LEMMA': 'algorithm'}]
 +
* [{'LOWER': 'stochastic'}, {'LEMMA': 'algorithm'}]

2021년 2월 16일 (화) 23:36 기준 최신판

노트

위키데이터

말뭉치

  1. An algorithm that uses random numbers to decide what to do next anywhere in its logic is called Randomized Algorithm.[1]
  2. The process of designing and analyzing a randomized algorithm focuses on establishing that it is likely to behave “well” on every input.[2]
  3. With a randomized algorithm, in contrast, no assumption is made about the input.[2]
  4. A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic.[3]
  5. Computational complexity theory models randomized algorithms as probabilistic Turing machines.[3]
  6. The study of randomized algorithms was spurred by the 1977 discovery of a randomized primality test (i.e., determining the primality of a number) by Robert M. Solovay and Volker Strassen.[3]
  7. Soon afterwards Michael O. Rabin demonstrated that the 1976 Miller's primality test can be turned into a randomized algorithm.[3]
  8. We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data.[4]
  9. Starting in the 1970's, many of the most significant results were randomized algorithms solving basic compuatational problems that had (to that time) resisted efficient deterministic computation.[5]
  10. This raises the question, can such results be obtained for all randomized algorithms?[5]
  11. This book introduces the basic concepts in the design and analysis of randomized algorithms.[6]
  12. Over the past 25 years the design and analysis of randomized algorithms, which make random choices during their execution, has become an integral part of algorithm theory.[7]
  13. For many problems, surprisingly elegant and fast randomized algorithms can be developed.[7]
  14. A randomized algorithm is an algorithm that makes random choices as part of its logic.[8]
  15. CTR6, CEE3.1, CEE3.2, CG1, Examine conditions under which randomized algorithms can be used.[8]
  16. In this course, we will introduce you to the foundations of randomized algorithms and probabilistic analysis of algorithms.[9]
  17. We design a randomized algorithm for consensus pattern problem.[10]
  18. (3) We develop a software tool, MotifDetector, that uses our randomized algorithm to find good seeds and uses the improved EM algorithm to do local search.[10]

소스

  1. ↑ Randomized Algorithms
  2. ↑ 2.0 2.1 An Overview of Randomized Algorithms
  3. ↑ 3.0 3.1 3.2 3.3 Randomized algorithm
  4. ↑ Paper
  5. ↑ 5.0 5.1 Can every randomized algorithm be derandomized?
  6. ↑ Randomized Algorithms | Algorithmics, complexity, computer algebra and computational geometry
  7. ↑ 7.0 7.1 Randomized Algorithms
  8. ↑ 8.0 8.1 FIB - Barcelona School of Informatics
  9. ↑ Max-Planck-Institut für Informatik: Randomized Algorithms and Probabilistic Analysis of Algorithms
  10. ↑ 10.0 10.1 RANDOMIZED ALGORITHMS FOR MOTIF DETECTION

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'randomized'}, {'LEMMA': 'algorithm'}]
  • [{'LOWER': 'stochastic'}, {'LEMMA': 'algorithm'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=확률적_알고리즘&oldid=51003"