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IanLewis id:IanLewis

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Boyerâ€“Moore - Wikipedia
IanLewis 2010/06/03
The BoyerÃ¢Â€Â“Moore string search algorithm is a particularly efficient string searching algorithm, and it has been the standard benchmark for the practical string search literature.[1] It was developed by Bob Boyer and J Strother Moore in 1977. The algorithm preprocesses the target string (key) th

algorithm

string

search

text-processing
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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
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Rolling with Ruby on Rails
Now, next, and beyond: Tracking need-to-know trends at the intersection of business and techno logy AI/ML Few techno logies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML). Future of the Firm Everything from new organizational structures and payment schemes to new expectations, skills, and tools will shape the future of the fi
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
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Naive Bayes classifier - Wikipedia
Example of a naive Bayes classifier depicted as a Bayesian Network In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models.[1] Naive
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
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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
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Bayesian probability - Wikipedia
Bayesian probability (/ËˆbeÉªziÉ™n/ BAY-zee-É™n or /ËˆbeÉªÊ’É™n/ BAY-zhÉ™n)[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
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Bayesian average - Wikipedia
A Bayesian average is a method of estimating the mean of a population using outside information, especially a pre-existing belief,[1] which is factored into the calculation. This is a central feature of Bayesian interpretation. This is useful when the available data set is small.[2] Calculating the Bayesian average uses the prior mean m and a constantÂ C. CÂ is chosen based on the typical data set s
IanLewis 2010/01/18
A Bayesian average is a method of estimating the mean of a population consistent with Bayesian interpretation, where instead of estimating the mean strictly from the available data set, other existing information related to that data set may also be incorporated into the calculation in order to mini

bayesian

average

rating

ranking

algorithm

bayes
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Currying - Wikipedia
This article is about the mathematical technique. For the cooking process of this name, see Curry. For the leather finishing process, see Currier. For horse grooming, see Currycomb. In mathematics and computer science, currying is the technique of translating a function that takes multiple arguments into a sequence of families of functions, each taking a single argument. In the prototypical exampl
IanLewis 2009/11/22
In mathematics and computer science, currying or SchÃƒÂ¶nfinkelisation, invented by Moses SchÃƒÂ¶nfinkel and later re-invented by Haskell Curry,[1] is the technique of transforming a function that takes multiple arguments (or an n-tuple of arguments) in such a way that it can be called as a chain of

programming

functional

currying

lambda

algorithm

languages
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linkiblog | How to Build a Popularity Algorithm You can be Proud of
<< Asp.net Localization: How to Access a Local Resource from Outside the Page | Many web sites allow users to casts vote on it ems. These visitors' votes are then often used to detect the it ems' "popularity" and hence rank the rated it ems accordingly. And when "rank" comes into play things gets tricky: The system can have inherent deficiencies in ranking it ems. That is mostly because develope
IanLewis 2009/09/08
Many web sites allow users to casts vote on items. These visitors' votes are then often used to detect the items' "popularity" and hence rank the rated items accordingly.

ranking

programming

statistics

math

development

algorithm

popularity

voting

bayes

bayesian
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