The document describes the website of Professor Takigawa from Hokkaido University. The website provides information about the professor's research interests and activities in the field of artificial intelligence and robotics. It contains links to publications, projects, and contact details.
データマイニングや機械学習をやるときによく問題となる「リーケージ」を防ぐ方法について論じた論文「Leakage in Data Mining: Formulation, Detecting, and Avoidance」(Kaufman, Shachar, et al., ACM Transactions on Knowledge Discovery from Data (TKDD) 6.4 (2012): 1-21.)を解説します。
主な内容は以下のとおりです。
・過去に起きたリーケージの事例の紹介
・リーケージを防ぐための2つの考え方
・リーケージの発見
・リーケージの修正
The document describes the website of Professor Takigawa from Hokkaido University. The website provides information about the professor's research interests and activities in the field of artificial intelligence and robotics. It contains links to publications, projects, and contact details.
データマイニングや機械学習をやるときによく問題となる「リーケージ」を防ぐ方法について論じた論文「Leakage in Data Mining: Formulation, Detecting, and Avoidance」(Kaufman, Shachar, et al., ACM Transactions on Knowledge Discovery from Data (TKDD) 6.4 (2012): 1-21.)を解説します。
主な内容は以下のとおりです。
・過去に起きたリーケージの事例の紹介
・リーケージを防ぐための2つの考え方
・リーケージの発見
・リーケージの修正
第6回 統計・機械学習若手シンポジウムの公演で使用したユーザーサイド情報検索システムについてのスライドです。
https://sites.google.com/view/statsmlsymposium21/
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) https://arxiv.org/abs/2105.12353
Retrieving Black-box Optimal Images from External Databases (WSDM 2022) https://arxiv.org/abs/2112.14921
- The document discusses linear regression models and methods for estimating coefficients, including ordinary least squares and regularization methods like ridge regression and lasso regression.
- It explains how lasso regression, unlike ordinary least squares and ridge regression, has the property of driving some of the coefficient estimates exactly to zero, allowing for variable selection.
- An example using crime rate data shows how lasso regression can select a more parsimonious model than other methods by setting some coefficients to zero.
第6回 統計・機械学習若手シンポジウムの公演で使用したユーザーサイド情報検索システムについてのスライドです。
https://sites.google.com/view/statsmlsymposium21/
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) https://arxiv.org/abs/2105.12353
Retrieving Black-box Optimal Images from External Databases (WSDM 2022) https://arxiv.org/abs/2112.14921
- The document discusses linear regression models and methods for estimating coefficients, including ordinary least squares and regularization methods like ridge regression and lasso regression.
- It explains how lasso regression, unlike ordinary least squares and ridge regression, has the property of driving some of the coefficient estimates exactly to zero, allowing for variable selection.
- An example using crime rate data shows how lasso regression can select a more parsimonious model than other methods by setting some coefficients to zero.