assifier has a stable learning algorithm if, by changing the tr t data don't change. For instance, the predictions of a decisi change in the training data. This definition depends on the nstable with 103 training examples may be stable with 109 nswers of unstable classifiers trained over multiple training t independent, generally sampled with replacement from the nverting weak classifier (very simple models) to strong ones s between the inputs and the class labels). A weak learner i tributes x; is only slightly correlated with its true class t₁. tter than random, but not much better than random. In b
assifier has a stable learning algorithm if, by changing the tr t data don't change. For instance, the predictions of a decisi change in the training data. This definition depends on the nstable with 103 training examples may be stable with 109 nswers of unstable classifiers trained over multiple training t independent, generally sampled with replacement from the nverting weak classifier (very simple models) to strong ones s between the inputs and the class labels). A weak learner i tributes x; is only slightly correlated with its true class t₁. tter than random, but not much better than random. In b
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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