arXiv:2410.18950
Adjusted Overfitting Regression
원문 초록
In this paper, I will introduce a new form of regression, that can adjust overfitting and underfitting through, "distance-based regression." Overfitting often results in finding false patterns causing inaccurate results, so by having a new approach that minimizes overfitting, more accurate predictions can be derived. Then I will proceed with a test of my regression form and show additional ways to optimize the regression. Finally, I will apply my new technique to a specific data set to demonstrate its practical value.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Active Learning for Manifold Gaussian Process Regression
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
GRASP: Grouped Regression with Adaptive Shrinkage Priors
CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization
Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset
Fidelity Isn't Accuracy: When Linearly Decodable Functions Fail to Match the Ground Truth
토론
이 논문과 연결된 포럼 글이 아직 없습니다.