noteJ

noteJ

  • 분류 전체보기 (27)
    • Machine Learning (26)
    • Django (1)
  • 홈
  • 태그
  • 방명록
RSS 피드
로그인
로그아웃 글쓰기 관리

noteJ

컨텐츠 검색

태그

Nearest Neighbor Classifier discretization association rules Automated Test Numeric underflow zero count stratified sampling feature subset selection pre processing Pre-processing Apriori algorithm Dimensionality Reduction data-mining feature creation MLP calculation Naïve Bayes classifier variable transformation LVQ multiple classification Curse of dimensionality

최근글

댓글

공지사항

아카이브

KNN(1)

  • Difference between KNN and LVQ

    KNN (K-Nearest Neighbors) and LVQ (Learning Vector Quantization) are both popular machine learning algorithms for classification tasks, but they differ in their approach and methodology. KNN is a non-parametric and lazy learning algorithm, which means that it does not make any assumptions about the underlying distribution of the data and does not learn a model from the training data. Instead, KN..

    2023.03.07
이전
1
다음
티스토리
© 2018 TISTORY. All rights reserved.

티스토리툴바