Difference between revisions of "Bayes"
(Created page with "[http://www.youtube.com/results?search_query=naive+bayes+artificial+intelligence Youtube search...] A Naive Bayes classifier assumes that the presence of a particular feature...") |
|||
| Line 1: | Line 1: | ||
[http://www.youtube.com/results?search_query=naive+bayes+artificial+intelligence Youtube search...] | [http://www.youtube.com/results?search_query=naive+bayes+artificial+intelligence Youtube search...] | ||
| + | |||
| + | * [[AI Solver]] | ||
| + | ** [[...predict categories]] | ||
| + | * [[Capabilities]] | ||
| + | |||
| + | __________________________________________________________ | ||
A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’. | A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’. | ||
| + | |||
| + | <youtube>M59h7CFUwPU</youtube> | ||
| + | <youtube>CPqOCI0ahss</youtube> | ||
| + | |||
| + | == Two-Class Bayes Point Machine == | ||
| + | [http://www.youtube.com/results?search_query=two+class+bayes+point+machine+artificial+intelligence Youtube search...] | ||
| + | |||
| + | This algorithm efficiently approximates the theoretically optimal Bayesian average of linear classifiers (in terms of generalization performance) by choosing one "average" classifier, the Bayes Point. Because the Bayes Point Machine is a Bayesian classification model, it is not prone to overfitting to the training data. | ||
| + | |||
| + | * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-bayes-point-machine Two-Class Bayes Point Machine | Microsoft] | ||
<youtube>M59h7CFUwPU</youtube> | <youtube>M59h7CFUwPU</youtube> | ||
<youtube>CPqOCI0ahss</youtube> | <youtube>CPqOCI0ahss</youtube> | ||
Revision as of 21:33, 2 June 2018
__________________________________________________________
A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
Two-Class Bayes Point Machine
This algorithm efficiently approximates the theoretically optimal Bayesian average of linear classifiers (in terms of generalization performance) by choosing one "average" classifier, the Bayes Point. Because the Bayes Point Machine is a Bayesian classification model, it is not prone to overfitting to the training data.
