'Redundant features' diaporamas de présentation

Problem

Text Mining, Information and Fact Extraction Part 2: Machine Learning Techniques Marie-Francine Moens Department of Computer Science Katholieke Universiteit Leuven, Belgium sien.moens@cs.kuleuven.be. Problem.

749 views • 62 slides

Redundant Feature Elimination for Multi-Class Problems

644 views • 50 slides

Ensemble Classifiers

Ensemble Classifiers. Prof. Navneet Goyal. Ensemble Classifiers. Introduction & Motivation Construction of Ensemble Classifiers Boosting (Ada Boost) Bagging Random Forests Empirical Comparison. Introduction & Motivation. Suppose that you are a patient with a set of symptoms

546 views • 37 slides

A genetic interaction map of drug responses in cancer cell lines Felix Klein (Huber group)

A genetic interaction map of drug responses in cancer cell lines Felix Klein (Huber group). Marco Breinig. Synthetic genetic interactions. Gene A. Gene A. Gene A. Gene A. Gene B. Gene B. Gene B. Gene B. viable. viable. viable. lethal. Relevance to cancer. Gene A. Gene A. Gene A.

428 views • 24 slides

Feature Selection

Feature Selection. Dr. Gheith Abandah. Definition. Feature selection is typically a search problem for finding an optimal or suboptimal subset of m features out of original M features. Benefits: For excluding irrelevant and redundant features,

433 views • 21 slides

Faten Hussein

The University of British Columbia Department of Electrical & Computer Engineering. Feature Selection and Weighting using Genetic Algorithm for Off-line Character Recognition Systems. Presented by. Faten Hussein. Outline. Introduction & Problem Definition Motivation & Objectives

315 views • 20 slides

Fuzzy Entropy based feature selection for classification of hyperspectral data Mahesh Pal Department of Civil Engineerin

Fuzzy Entropy based feature selection for classification of hyperspectral data Mahesh Pal Department of Civil Engineering National Institute of Technology Kurukshetra. Hyperspectral data

354 views • 18 slides

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 21 Linear discrimination, linear machines W?odzis?aw Duch Dept. of Informatics, UMK Google: W Duch. Regression and model trees. Regression: numeric, continuous classes C(X) , predict number. .

270 views • 15 slides

Feature redundancy based on logical coverage

1. 1. 1. 1. 2. Feature redundancy based on logical coverage In classification rule learning, good features discriminate one class from another. Redundant features are those which are covered by another, better discriminating, feature.

151 views • 1 slides