Explore >> Select a destination
|
You are here 
|
www.ericekholm.com | ||
| | | | |
www.arrsingh.com
|
|
| | | | | Linear Regression predicts the value of a dependent variable (y) given one or more independent variables (x1, x2, x3...xn). In this case, y is continuous - i.e. it can hold any value. In many real world problems[1], however, we often want to predict a binary value instead | |
| | | | |
fa.bianp.net
|
|
| | | | | MathJax.Hub.Config({ extensions: ["tex2jax.js"], jax: ["input/TeX", "output/HTML-CSS"], tex2jax: { inlineMath: [ ['$','$'], ["\\(","\\)"] ], displayMath: [ ['$$','$$'], ["\\[","\\]"] ], processEscapes: true }, TeX: { equationNumbers: { autoNumber: "AMS" }, extensions: ["AMSmath.js", "AMSsymbols.js"] }, "HTML-CSS": { fonts: ["TeX"] } }); In this post I compar several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization ... | |
| | | | |
matbesancon.xyz
|
|
| | | | | Learning by doing: predicting the outcome. | |
| | | | |
fabricebaudoin.blog
|
|
| | | In this lecture, we studySobolev inequalities on Dirichlet spaces. The approach we develop is related to Hardy-Littlewood-Sobolev theory The link between the Hardy-Littlewood-Sobolev theory and heat kernel upper bounds is due to Varopoulos, but the proof I give below I learnt it from my colleague RodrigoBañuelos. It bypasses the Marcinkiewicz interpolation theorem,that was originally used... | ||
