목록Reference (46)
Linear and Quadratic Discriminant Analysis: Tutorial
Linear and Quadratic Discriminant Analysis: Tutorial https://arxiv.org/pdf/1906.02590.pdf
Positive Definite Matrix
Positive Definite Matrix https://bookdown.org/ts_robinson1994/10EconometricTheorems/pd.html Chapter 9 Positive Definite Matrices | 10 Fundamental Theorems for Econometrics This book walks through the ten most important statistical theorems as highlighted by Jeffrey Wooldridge, presenting intuiitions, proofs, and applications. bookdown.org Positive Definite Matrix & Symmetric and Idempotent https..
Singular Value Decomposition & Optimization
https://darkpgmr.tistory.com/106 [선형대수학 #4] 특이값 분해(Singular Value Decomposition, SVD)의 활용 활용도 측면에서 선형대수학의 꽃이라 할 수 있는 특이값 분해(Singular Value Decomposition, SVD)에 대한 내용입니다. 보통은 복소수 공간을 포함하여 정의하는 것이 일반적이지만 이 글에서는 실수(real darkpgmr.tistory.com https://darkpgmr.tistory.com/149 최적화 기법의 직관적 이해 일전에 최적화 기법에 대해 정리하는 글(기계학습 - 함수 최적화 기법 정리)을 썼었는데, 지금에 와서 보니 너무 수식만 가득한 글이었던 것 같습니다. 그래서 수식보다는 좀더 직관적으로 이해 darkpg..
Proof : Gauss Markov theorem
https://pages.stat.wisc.edu/~st849-1/lectures/Ch04.pdf https://math.stackexchange.com/questions/1542602/proof-of-gauss-markov-theorem Proof of Gauss-Markov theorem Theorem: Let $Y=X\beta+\varepsilon$ where $$Y\in\mathcal M_{n\times 1}(\mathbb R),$$ $$X\in \mathcal M_{n\times p}(\mathbb R),$$ $$\beta\in\mathcal M_{n\times 1}(\mathbb R ),$$ and $$\varepsilon\in\ math.stackexchange.com https://math..
Ridge & LASSO property reference
1. ridge regression https://arxiv.org/pdf/1509.09169.pdf https://aldosolari.github.io/SL/docs/SLIDES/Ridge.pdf 2. Ridge & LASSO https://piazza.com/class_profile/get_resource/iwgts4iuaq41h1/iyjsyz3hdhtdf https://www.hse.ru/data/2018/03/15/1164355911/4.%20Ridge%20Regression%20and%20Lasso%20v1.pdf
Kernel PCA
kernel pca lecture note https://www.cs.mcgill.ca/~dprecup/courses/ML/Lectures/ml-lecture13.pdf Machine Learning Lecture notes https://www.cs.mcgill.ca/~dprecup/courses/ML/lectures.html Machine Learning (COMP-652 and ECSE-608) Mar.16 Method of moments, latent variable models, and tensors Lecture 19 slides JMLR paper www.cs.mcgill.ca
Bootstrap for time series data
https://www.ine.pt/revstat/pdf/Randomforestsfortimeseries.pdf https://medium.com/@daydreamersjp/bootstrapping-on-time-series-data-moving-block-bootstrap-79aaf6648aec Bootstrapping on Time Series Data — “Moving Block Bootstrap” Bootstrapping on the data that has serial correlation medium.com
Support Vector Machine(Kernel)
Kernel https://www.analytixlabs.co.in/blog/introduction-support-vector-machine-algorithm/ Introduction To SVM - Support Vector Machine Algorithm in Machine Learning A simple guide to understand what is a Support vector machine algorithm and how it works as Supervised learning in Machine learning. Get insights into it from the experts. www.analytixlabs.co.in SVM in Hilbert space https://www.cs.cm..