Explore >> Select a destination
|
You are here 
|
chris-said.io | ||
| | | | |
www.jeremykun.com
|
|
| | | | | Machine learning is broadly split into two camps, statistical learning and non-statistical learning. The latter we've started to get a good picture of on this blog; we approached Perceptrons, decision trees, and neural networks from a non-statistical perspective. And generally "statistical" learning is just that, a perspective. Data is phrased in terms of independent and dependent variables, and statistical techniques are leveraged against the data. In this post we'll focus on the simplest example of thi... | |
| | | | |
www.econometricsbysimulation.com
|
|
| | | | | Simulations, Econometrics, Stata, R,intelligent mulit-agent systems, Psychometrics, latent modelling, maximization, statistics, quantitative methods. | |
| | | | |
gregorygundersen.com
|
|
| | | | | ||
| | | | |
francisbach.com
|
|
| | | [AI summary] The blog post discusses the spectral properties of kernel matrices, focusing on the analysis of eigenvalues and their estimation using tools like the matrix Bernstein inequality. It also covers the estimation of the number of integer vectors with a given L1 norm and the relationship between these counts and combinatorial structures. The post includes a detailed derivation of bounds for the difference between true and estimated eigenvalues, highlighting the role of the degrees of freedom and the impact of regularization in kernel methods. Additionally, it touches on the importance of spectral analysis in machine learning and its applications in various domains. | ||
