[B! algorithm][machine-learning] IanLewisのブックマーク

IanLewis id:IanLewis

algorithmとmachine-learningに関するIanLewisのブックマーク (6)

Decision tree learning - Wikipedia
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represen
IanLewis 2010/01/19
Decision tree learning, used in data mining and machine learning, uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. More descriptive names for such tree models are classification trees or regression trees. In these tree str

machine-learning

algorithm

data-mining

decision-tree
リンク
Rolling with Ruby on Rails
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IanLewis 2010/01/19
Decision trees are a topic of artificial intelligence. More specifically, they belong to the subfield of machine learning. This is due to their ability to learn--through example--to classify individual records in a data set.

algorithm

statistics

machine-learning

python
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Boost - Wikipedia
IanLewis 2010/01/19
Boosting is a machine learning meta-algorithm for performing supervised learning.

boosting

machine-learning

algorithm
リンク
Naive Bayes classifier - Wikipedia
Example of a naive Bayes classifier depicted as a Bayesian Network In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class.[1] In other words, a naive Bayes model assumes the information about the class provided by each variable is unrelated to the informat
IanLewis 2010/01/18
A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".

bayes

bayesian

algorithm

naive

classifier

machine-learning
リンク
Bayesian inference - Wikipedia
Toggle Inference over exclusive and exhaustive possibilities subsection
IanLewis 2010/01/18
Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name "Bayesian" comes from the frequent use of Bayes' theorem in the inference process. Bayes' theorem was derived from the work of th

algorithm

bayes

bayesian

inference

machine-learning
リンク
Bayesian probability - Wikipedia
Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən)[1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation[2] representing a state of knowledge[3] or as quantification of a personal belief.[4] The Bayesian interpretation of probability can be seen as an extension of
IanLewis 2010/01/18
Bayesian probability is one of the most popular interpretations of the concept of probability. The Bayesian interpretation of probability can be seen as an extension of logic that enables reasoning with uncertain statements. To evaluate the probability of a hypothesis, the Bayesian probabilist speci

probability

bayes

bayesian

algorithm

ranking

machine-learning
リンク
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