'Decision tree algorithm' presentation slideshows

Data Mining Techniques Outline

Data Mining Techniques Outline. Goal: Provide an overview of basic data mining techniques. Statistical Point Estimation Models Based on Summarization Bayes Theorem Hypothesis Testing Regression and Correlation Similarity Measures Decision Trees Neural Networks Activation Functions

960 views • 45 slides

Subspace Clustering for High Dimensional Data: A Review

Subspace Clustering for High Dimensional Data: A Review. Advisor : Dr. Hsu Presenter : Jing-Wei Lin. Outline. Motivation Objective Introduction & B ackground Dimensionality Reduction Subspace Clustering Theory Subspace Clustering Methods Empirical Comparison Applications Conclusion.

414 views • 39 slides

Decision Trees

Decision Trees. Jeff Storey. Overview. What is a Decision Tree Sample Decision Trees How to Construct a Decision Tree Problems with Decision Trees Decision Trees in Gaming Summary. What is a Decision Tree?. An inductive learning task

805 views • 38 slides

Decision Trees

Decision Trees. Jeff Storey. Overview. What is a Decision Tree Sample Decision Trees How to Construct a Decision Tree Problems with Decision Trees Decision Trees in Gaming Summary. What is a Decision Tree?. An inductive learning task

633 views • 38 slides

Presented by Miss. Rakwarinn Wannasin and Mr.Krittachai Boonsivanon

A Mobile Learning by Decision Tree for Provisional Diagnosis on Smartphone. Presented by Miss. Rakwarinn Wannasin and Mr.Krittachai Boonsivanon. Outline. Background. Related works. Objectives. Methodology. Result. Conclusion.

432 views • 28 slides

Decision Tree Algorithm

Decision Tree Algorithm. Comp328 tutorial 1 Kai Zhang. Outline. Introduction Example Principles Entropy Information gain Evaluations Demo. The problem. Given a set of training cases/objects and their attribute values, try to determine the target attribute value of new examples.

538 views • 25 slides

ICS 421 Spring 2010 Data Mining 2

ICS 421 Spring 2010 Data Mining 2. Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa. Beyond Co-Occurrences. How do you obtain rules to predict future high risk customers? . Predictor attributes.

234 views • 16 slides

???? ?? ???? - Development of Decision Tree Algorithm for Semantic Web data -

???? ?? ???? - Development of Decision Tree Algorithm for Semantic Web data -. 2010313148 ???. Agenda. Project Purpose Motivation Related Work Algorithm Description of Problem. 1. Project Purpose. Relational Data - Semantic Web Data (Linked data) -. Semantic Decision Tree.

359 views • 16 slides

Machine Learning in Football

831 views • 15 slides

An Introduction to Machine Learning Algorithms

Machine Learning is the hottest trend in the IT industry, Machine Learning is quite helpful in making predictions based on past collection of data. Behind the scene, things work with help of complex algorithms that make it happen!

217 views • 14 slides