<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bias on felladog</title><link>https://utsavdarlami.github.io/blogs/tags/bias/</link><description>Recent content in Bias on felladog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Tue, 13 Jul 2021 21:16:35 +0545</lastBuildDate><atom:link href="https://utsavdarlami.github.io/blogs/tags/bias/index.xml" rel="self" type="application/rss+xml"/><item><title>Inductive Bias</title><link>https://utsavdarlami.github.io/blogs/notes/2021-06-03--11-07-01z--inductive_bias/</link><pubDate>Thu, 03 Jun 2021 16:52:00 +0545</pubDate><guid>https://utsavdarlami.github.io/blogs/notes/2021-06-03--11-07-01z--inductive_bias/</guid><description>References :
Reading: Tom Mitchell Lectures slides of chapter 2 Tom Mitchell, Machine Learning Chapter 2 To Read: https://en.wikipedia.org/wiki/Inductive%5Fbias Questions :
The inductive bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered.
Consider a concept learning algorithm L for the set of instances X. Let c be an arbitrary concept defined over X, and let \(D_c\) = {&amp;lt;x,c(x)&amp;gt;} be an arbitrary set of training examples of c.</description></item></channel></rss>