The cardinality of the largest set that an algorithm of this type can shatter is called the Vapnik-Chervonenkis dimension, a measure of the effectiveness of these algorithms. A meta-algorithm in this field won the Godel prize for Schapire and Freund. Those two men wrote a seminal text in this field titled Boosting, which responds to the question of whether weak algorithms of this type can be combined into a strong one. Algorithms of this type that will most likely have a low error were identified by Leslie Valiant as being (*) "Probably Approximately Correct." One of the fundamental problems i
The cardinality of the largest set that an algorithm of this type can shatter is called the Vapnik-Chervonenkis dimension, a measure of the effectiveness of these algorithms. A meta-algorithm in this field won the Godel prize for Schapire and Freund. Those two men wrote a seminal text in this field titled Boosting, which responds to the question of whether weak algorithms of this type can be combined into a strong one. Algorithms of this type that will most likely have a low error were identified by Leslie Valiant as being (*) "Probably Approximately Correct." One of the fundamental problems in this field is classification, and algorithms in this field can use decision tree models or artificial neural networks. These algorithms may be unsupervised or supervised, referring to whether outputs are checked for training data. For 10 points, name this field concerning systems that can gather information from data and update behavior accordingly.
The cardinality of the largest set that an algorithm of this type can shatter is called the Vapnik-Chervonenkis dimension, a measure of the effectiveness of these algorithms.
machine learning [accept classification before mentioned; prompt on “artificial intelligence”]