December 2012
Intermediate to advanced
407 pages
10h 18m
English
Content preview from Pattern Recognition and Machine Learning
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9
Learning by Classification and Discovery
KEYWORDS
massive data
decision tree
information theory
noisy data
simultaneous presentation
incremental presentation
Bayes
theorem
concept representation space
learning by discovery
problem solving
heuristic search
discovery of attributes
proportionality graph search
suspended node
heuristic knowledge
frame representation
heuristic rule
meta rule
This chapter will explain methods for learning by classification and discovery. The first half of this chapter describes a classification method using decision trees and Bayesian statistics. A decision tree can be generated by a simple algorithm and is an efficient method of finding regularities of massive data. Bayesian statistics can be applied to a learning ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.