'Classification task' presentation slideshows

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4 By Gun Ho Lee ghlee@ssu.ac.kr Intelligent Information Systems Lab Soongsil University , Korea

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Classification: Definition. Given a collection of records ( training set ) Each record contains a set of attributes , one of the attributes is the class .

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Outline. General Approach to Solving a Classification Problem Decision Tree Induction Model Overfitting Evaluating the Performance of a Classifier

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

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Classification: Basic Concepts, Decision Trees, and Model Evaluation

Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4 Introduction to Data Mining By Tan, Steinbach, Kumar. Edited by Dr . Panagiotis S ymeonidis Data Engineering Laboratory. http://delab.csd.auth.gr/~symeon. 1. Classification: Definition.

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Classification: Basic Concepts and Decision Trees

Classification: Basic Concepts and Decision Trees. A programming task. Classification: Definition. Given a collection of records ( training set ) Each record contains a set of attributes , one of the attributes is the class .

728 views • 71 slides

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Classification: Definition. Given a collection of records ( training set ) Each record contains a set of attributes , one of the attributes is the class .

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Classification: Definition

Classification: Definition. Given a collection of records ( training set ) Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible.

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Chapter 4 Classification

Chapter 4 Classification. Classification: Definition. Given a collection of records ( training set ) Each record contains a set of attributes , one of the attributes is the class . Find a model for class attribute as a function of the values of other attributes.

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Computational Biology

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Manifold-Enhanced Compressive Measurement

Manifold-Enhanced Compressive Measurement. Richard Baraniuk Aswin Sankaranarayanan Chinmay Hegde. Research Highlight. 500:1. In an image classification task , exploiting manifold signal prior improves resilience to noise by 500:1 (27dB). CoM System Architecture. Why CM Works.

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Dual Active Feature and Sample Selection for Graph Classification

Dual Active Feature and Sample Selection for Graph Classification. Xiangnan Kong 1 , Wei Fan 2 , Philip S. Yu 1. 1 Department of Computer Science University of Illinois at Chicago 2 IBM T. J. Watson Research. KDD 2011. Graph Classification. Traditional Classification:. Feature Vector.

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Artificial Neural Networks

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Cognitive Modelling Project

Cognitive Modelling Project. Aoife Kilduff 06355251. Classification Task. Classification involves generalizing from particular learned instances to novel situations This task: generalize information from training items (symptoms and diseases) to test items. How do people do this?

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Outlines

High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic. Presenter : Min-Cong Wu Authors : Lam Hong Lee, Dino Isa, Wou Onn Choo , Wen Yeen Chue 2012.ESA. Outlines. Motivation Objectives Methodology Experiments

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Data Mining Technique

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