<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
  <channel>
    <title>MATH &amp;amp; ML</title>
    <link>https://blogyong.tistory.com/</link>
    <description>배운대로 이해한대로 정리합니다
따라서 잘못된 내용이 있을 수 있습니다</description>
    <language>ko</language>
    <pubDate>Thu, 7 May 2026 06:45:26 +0900</pubDate>
    <generator>TISTORY</generator>
    <ttl>100</ttl>
    <managingEditor>BlogYong</managingEditor>
    <image>
      <title>MATH &amp;amp; ML</title>
      <url>https://tistory1.daumcdn.net/tistory/2874558/attach/4427ffc5952348d1ad3ff55da3d30cc1</url>
      <link>https://blogyong.tistory.com</link>
    </image>
    <item>
      <title>Information geometry &amp;amp; KL divergence</title>
      <link>https://blogyong.tistory.com/46</link>
      <description>아이디어) KL 메져가 제일 좋고 유일하다 : KL은 bregdivergence에도 포함되어있고,&amp;nbsp;f-divergence에도 포함되어있다.
1) Bregman divergence
prob model $p(x;\theta)=\frac{\exp(-\theta\cdot x)}{z}$ (exponential family)
$\theta$ : natural parameter
$z=\sum_x \exp(-\theta\cdot x)$ : constant f..</description>
      <author>BlogYong</author>
      <guid isPermaLink="true">https://blogyong.tistory.com/46</guid>
      <comments>https://blogyong.tistory.com/46#entry46comment</comments>
      <pubDate>Sun, 3 Jul 2022 10:24:56 +0900</pubDate>
    </item>
    <item>
      <title>ML의 전반적인 가정 : 데이터의 distribution을 모른다</title>
      <link>https://blogyong.tistory.com/44</link>
      <description>ML의 전반적인 가정 : 데이터의 distribution을 모른다!!
(다 같은말)
데이터의 distribution
= true prob density function
= data generating prob density function
= underlying prob density function</description>
      <category>Machine Learning</category>
      <author>BlogYong</author>
      <guid isPermaLink="true">https://blogyong.tistory.com/44</guid>
      <comments>https://blogyong.tistory.com/44#entry44comment</comments>
      <pubDate>Mon, 27 Jun 2022 11:49:06 +0900</pubDate>
    </item>
    <item>
      <title>Window10에서 sublime text3를 이용하여 Latex 작성하기(Bibtex까지)</title>
      <link>https://blogyong.tistory.com/43</link>
      <description>참고한 사이트
1. https://hellbell.tistory.com/entry/Sublime-Text-3-Latex-Plugin-%EC%84%A4%EC%B9%98-in-Windows-7
(설치 설명)
2. https://www.lucypark.kr/blog/2011/09/19/compiling-bibtex/
&amp;nbsp;(bibtex 어떻게 만드는지)
3. https://sammorrell.co.uk/2016/08/14/beginning-with-bi..</description>
      <category>Etc.</category>
      <category>bibtex</category>
      <category>latex</category>
      <category>sublime text</category>
      <category>sublime text3</category>
      <category>sublimeText</category>
      <category>레이텍</category>
      <author>BlogYong</author>
      <guid isPermaLink="true">https://blogyong.tistory.com/43</guid>
      <comments>https://blogyong.tistory.com/43#entry43comment</comments>
      <pubDate>Mon, 23 Sep 2019 21:41:04 +0900</pubDate>
    </item>
    <item>
      <title>Decision Tree, Random Forest, Ensemble(Bagging vs Boosting) Xgboost</title>
      <link>https://blogyong.tistory.com/41</link>
      <description>1. Ensemble이란 여러 모델을 이용하여 데이터를 학습하고, 모든 모델의 예측 결과를 평균하여 예측하는 방법(앙상블=뭔가 통합? 합쳐서 함께 어우른다는 느낌)1-1) Bagging방법이란 Bootstrap Aggregation의 약자로, 병렬적인 Ensemble모델로서 random sampling을 통해 여러가지 예측모형을 만들어 이를 함께 이용하여 학습하는, Variance를 감소시키기 위해 쓰는 방법(Random Forest)1-2) Boost..</description>
      <category>Machine Learning</category>
      <category>Bagging</category>
      <category>boosting</category>
      <category>decision tree</category>
      <category>ensemble</category>
      <category>random forest</category>
      <category>xgboost</category>
      <author>BlogYong</author>
      <guid isPermaLink="true">https://blogyong.tistory.com/41</guid>
      <comments>https://blogyong.tistory.com/41#entry41comment</comments>
      <pubDate>Sat, 8 Sep 2018 18:19:27 +0900</pubDate>
    </item>
    <item>
      <title>알고리즘 몇 가지 팁</title>
      <link>https://blogyong.tistory.com/40</link>
      <description>1. for문에 따른 내용이 한줄이여도 for문 밑에 다음줄로 넘겨서! 대신 괄호는 생략해서2.if문으로 경우를 나눌때 A경우일때만 실행을 하고싶으면 if(~A)면 return; 으로 처리해서 if문 전체를 끝내버릴수 있도록 한다.return; 을 잘 활용해서 함수를 딱 그 시점에 종료하는걸로 사용하면 좋다.3. 딱 정확히 2가지로만 나뉘는 경우 if(A) a&amp;nbsp;else b 는 삼항연산자로 A? a:b 로 써주면 좋다.(예시)int factori..</description>
      <category>Algorithm</category>
      <author>BlogYong</author>
      <guid isPermaLink="true">https://blogyong.tistory.com/40</guid>
      <comments>https://blogyong.tistory.com/40#entry40comment</comments>
      <pubDate>Thu, 30 Aug 2018 17:34:02 +0900</pubDate>
    </item>
  </channel>
</rss>