# jbrownlee/CleverAlgorithmsMachineLearning

jbrownlee committed Feb 9, 2012
 @@ -0,0 +1,60 @@ +% The Clever Algorithms Project: http://www.CleverAlgorithms.com +% (c) Copyright 2011 Jason Brownlee. Some Rights Reserved. +% This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License. + +% Name +% The algorithm name defines the canonical name used to refer to the technique, in addition to common aliases, abbreviations, and acronyms. The name is used in terms of the heading and sub-headings of an algorithm description. +\section{Averaged One-Dependence Estimators} +\label{sec:aode} +\index{Averaged One-Dependence Estimators} +\index{AODE} + +% other names +% What is the canonical name and common aliases for a technique? +% What are the common abbreviations and acronyms for a technique? +\emph{Averaged One-Dependence Estimators, AODE} + +% Taxonomy: Lineage and locality +% The algorithm taxonomy defines where a techniques fits into the field, both the specific subfields of Computational Intelligence and Biologically Inspired Computation as well as the broader field of Artificial Intelligence. The taxonomy also provides a context for determining the relation- ships between algorithms. The taxonomy may be described in terms of a series of relationship statements or pictorially as a venn diagram or a graph with hierarchical structure. +\subsection{Taxonomy} +% To what fields of study does a technique belong? +Averaged One-Dependence Estimators is ... +% What are the closely related approaches to a technique? + + +% Strategy: Problem solving plan +% The strategy is an abstract description of the computational model. The strategy describes the information processing actions a technique shall take in order to achieve an objective. The strategy provides a logical separation between a computational realization (procedure) and a analogous system (metaphor). A given problem solving strategy may be realized as one of a number specific algorithms or problem solving systems. The strategy description is textual using information processing and algorithmic terminology. +\subsection{Strategy} +% What is the information processing objective of a technique? +% What is a techniques plan of action? + + +% Heuristics: Usage guidelines +% The heuristics element describe the commonsense, best practice, and demonstrated rules for applying and configuring a parameterized algorithm. The heuristics relate to the technical details of the techniques procedure and data structures for general classes of application (neither specific implementations not specific problem instances). The heuristics are described textually, such as a series of guidelines in a bullet-point structure. +\subsection{Heuristics} +% What are the suggested configurations for a technique? +% What are the guidelines for the application of a technique to a problem instance? + +\begin{itemize} + \item +\end{itemize} + +% sample script in R +\subsection{Code Listing} + + +% References: Deeper understanding +% The references element description includes a listing of both primary sources of information about the technique as well as useful introductory sources for novices to gain a deeper understanding of the theory and application of the technique. The description consists of hand-selected reference material including books, peer reviewed conference papers, journal articles, and potentially websites. A bullet-pointed structure is suggested. +\subsection{References} +% What are the primary sources for a technique? +% What are the suggested reference sources for learning more about a technique? + +% primary sources +\subsubsection{Primary Sources} +% seminal + +% more info +\subsubsection{More Information} + + +% END
 @@ -0,0 +1,62 @@ +% The Clever Algorithms Project: http://www.CleverAlgorithms.com +% (c) Copyright 2011 Jason Brownlee. Some Rights Reserved. +% This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License. + +% Name +% The algorithm name defines the canonical name used to refer to the technique, in addition to common aliases, abbreviations, and acronyms. The name is used in terms of the heading and sub-headings of an algorithm description. +\section{Least Angle Regression} +\label{sec:lars} +\index{Least Angle Regression} +\index{LARS} +\index{LAR} + +% other names +% What is the canonical name and common aliases for a technique? +% What are the common abbreviations and acronyms for a technique? +\emph{Least Angle Regression, LARS, LAR} + +% Taxonomy: Lineage and locality +% The algorithm taxonomy defines where a techniques fits into the field, both the specific subfields of Computational Intelligence and Biologically Inspired Computation as well as the broader field of Artificial Intelligence. The taxonomy also provides a context for determining the relation- ships between algorithms. The taxonomy may be described in terms of a series of relationship statements or pictorially as a venn diagram or a graph with hierarchical structure. +\subsection{Taxonomy} +% To what fields of study does a technique belong? +Least Angle Regression is a Regularization method for Multiple Linear Regression models. +% What are the closely related approaches to a technique? +Least Angle Regression is related to other Regularized Regression models such as the LASSO and Elastic Net. + +% Strategy: Problem solving plan +% The strategy is an abstract description of the computational model. The strategy describes the information processing actions a technique shall take in order to achieve an objective. The strategy provides a logical separation between a computational realization (procedure) and a analogous system (metaphor). A given problem solving strategy may be realized as one of a number specific algorithms or problem solving systems. The strategy description is textual using information processing and algorithmic terminology. +\subsection{Strategy} +% What is the information processing objective of a technique? +% What is a techniques plan of action? + + +% Heuristics: Usage guidelines +% The heuristics element describe the commonsense, best practice, and demonstrated rules for applying and configuring a parameterized algorithm. The heuristics relate to the technical details of the techniques procedure and data structures for general classes of application (neither specific implementations not specific problem instances). The heuristics are described textually, such as a series of guidelines in a bullet-point structure. +\subsection{Heuristics} +% What are the suggested configurations for a technique? +% What are the guidelines for the application of a technique to a problem instance? + +\begin{itemize} + \item +\end{itemize} + +% sample script in R +\subsection{Code Listing} + + +% References: Deeper understanding +% The references element description includes a listing of both primary sources of information about the technique as well as useful introductory sources for novices to gain a deeper understanding of the theory and application of the technique. The description consists of hand-selected reference material including books, peer reviewed conference papers, journal articles, and potentially websites. A bullet-pointed structure is suggested. +\subsection{References} +% What are the primary sources for a technique? +% What are the suggested reference sources for learning more about a technique? + +% primary sources +\subsubsection{Primary Sources} +% seminal +Efron et~al.\ describe Least Angle Regression (LARS) \cite{Efron2002}. + +% more info +\subsubsection{More Information} + + +% END
 @@ -53,6 +53,7 @@ \subsection{Further Reading} \newpage\begin{bibunit}\input{a_regularization/ridge_regression}\putbib\end{bibunit} \newpage\begin{bibunit}\input{a_regularization/lasso}\putbib\end{bibunit} +\newpage\begin{bibunit}\input{a_regularization/least_angle_regression}\putbib\end{bibunit} \newpage\begin{bibunit}\input{a_regularization/elastic_net}\putbib\end{bibunit}