arXiv:1203.5647
Polynomial expansion of the binary classification function
원문 초록
This paper describes a novel method to approximate the polynomial coefficients of regression functions, with particular interest on multi-dimensional classification. The derivation is simple, and offers a fast, robust classification technique that is resistant to over-fitting.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLSF - Learning Label Specific Features for Multi-Label Classifcation
Inherently Faithful Attention Maps for Vision Transformers
Detecting immune cells with label-free two-photon autofluorescence and deep learning
Adversarial Surrogate Risk Bounds for Binary Classification
An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular Data
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
토론
이 논문과 연결된 포럼 글이 아직 없습니다.