Game Theory and Business Strategy: Extensive Form Game Solver Applet
http://www.gametheory.net/Mike/applets/ExtensiveForm/ An applet which solves extensive form games (multi-player decision trees). Extensive form game solver. Part of Mike Shor's lecture notes for a course in Game Theory.Interactive applets on Game Theory .net . Extensive Form Game Applet Solve sequential games and game trees This applet allows you to create extensive-form (sequential) games, and have them automatically solved for you. The applet allows up to four players, and up to 14 periods. To use the Knowledge Creation > Problem Solving > Decision Trees Game Theory Business Strategy extensive form game solver online applet simulation
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Other links at Knowledge Creation > Problem Solving > Decision Trees
1. RainForest
A Framework for Fast Decision Tree Construction of Large Datasets
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Lecture about decision trees by Amos Storkey.
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Chapter Six of Introduction to Machine Learning - Notes by Nils J. Nilsson.
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SGI's MineSet Tree Visualizer for viewing decision-trees.
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The MLC++ decision-trees can be viewed using SGI's MineSet Tree Visualizer
The MLC++ decision-trees can be viewed using SGI's MineSet TM Tree Visualizer. The Tree Visualizer allows one to navigate (fly) through the tree, zoom in on interesting nodes, click on bars to get counts, and mark interesting places in the tree. Universities participating in SGI's varsity
The MLC++ decision-trees can be viewed using SGI's MineSet TM Tree Visualizer. The Tree Visualizer allows one to navigate (fly) through the tree, zoom in on interesting nodes, click on bars to get counts, and mark interesting places in the tree. Universities participating in SGI's varsity
Tutorial Slides by Andrew Moore. A self-contained introduction to the flavor and terminology of data mining.
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The Decision Tree is one of the most popular classification algorithms
in current use in Data Mining and Machine Learning. This tutorial can
be used as a self-contained introduction to the flavor and
terminology of data mining without needing to review many statistical
or probabilistic
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