Download

1 / 29

Ensemble Learning 2, From Tree to Forest

Sep 17, 2012

290 likes | 499 Views

Motivation. To estimate complex response surface and class boundary.Reminder: SVM achieves this goal by the kernel trick;Boosting achieves this goal by combining weak classifiersClassification tree achieves this goal by generating complex surface/boundary in a very complex model.Bagging cl

Share Presentation

Embed Code

Link

melchior

Download Presentation

Ensemble Learning 2, From Tree to Forest

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

1. Ensemble Learning (2),From Tree to Forest Classification and Regression Tree Bagging of trees Random Forest

Forest tree seed research

Forest tree seed research. Research Organisations specialising in above tend to be …. Independent Isolated from seed research more widely &amp; Timber Industry/Forest Environment focused . DIFFICULTIES WITH TREE SEED RESEARCH. Poor viability Considerable dormancy

778 views • 38 slides

Ensemble Learning

Ensemble Learning. CS 6243 Machine Learning Modified from the slides by Dr. Raymond J. Mooney http://www.cs.utexas.edu/~mooney/cs391L And by Dr. Carla P. Gomes http://www.cs.cornell.edu/Courses/cs4700/2008fa/. Ensemble Learning.

1.75k views • 47 slides

Sparse vs. Ensemble Approaches to Supervised Learning

Sparse vs. Ensemble Approaches to Supervised Learning. Greg Grudic. Goal of Supervised Learning?. Minimize the probability of model prediction errors on future data Two Competing Methodologies Build one really good model Traditional approach Build many models and average the results

582 views • 43 slides

Ensemble Learning

Ensemble Learning. Lecturer: Dr. Bo Yuan E-mail: yuanb@sz.tsinghua.edu.cn. Real World Scenarios. VS. Real World Scenarios. What is ensemble learning?. Many individual learning algorithms are available: Decision Trees, Neural Networks, Support Vector Machines

868 views • 44 slides

Ensemble Learning

Ensemble Learning . what is an ensemble? why use an ensemble? selecting component classifiers selecting combining mechanism some results. . . . Classifier 1. Classifier 2. Classifier N. A Classifier Ensemble. Class Prediction. Combiner. Class Predictions. Input Features.

1.05k views • 15 slides

Ensemble Learning (2), Tree and Forest

Ensemble Learning (2), Tree and Forest. Classification and Regression Tree Bagging of trees Random Forest. Motivation. To estimate complex response surface and class boundary. Reminder: SVM achieves this goal by the kernel trick; Boosting achieves this goal by combining weak classifiers

522 views • 30 slides

From Evolutionary Computation to Ensemble Learning

From Evolutionary Computation to Ensemble Learning. Xin Yao CERCIA, School of Computer Science University of Birmingham UK. Overview. Introduction (Evolutionary Computation) Multi-objective learning and ensembles Online learning with concept drifts Class imbalance learning

627 views • 39 slides

Ensemble learning

Ensemble learning. David Kauchak CS451 – Fall 2013. Admin. Assignment 8 8.2 graded Hadoop / MapReduce : was it worthwhile? Final project. Ensemble learning. Basic idea: if one classifier works well, why not use multiple classifiers!. Ensemble learning.

1.09k views • 38 slides

Ensemble Learning: An Introduction

Ensemble Learning: An Introduction. Adapted from Slides by Tan, Steinbach, Kumar. General Idea. Why does it work?. Suppose there are 25 base classifiers Each classifier has error rate,  = 0.35 Assume classifiers are independent

561 views • 30 slides

Ensemble Learning

Ensemble Learning. Textbook, Learning From Examples, Section 10-12 (pp. 56-66). From the book Guesstimation. How much domestic trash and recycling is collected each year in the US (in tons)? Individual answers Confidence-weighted ensemble. Answer: 245 million tons. Ensemble learning.

609 views • 31 slides

Ensemble Learning

Ensemble Learning. Reading: R. Schapire , A brief introduction to boosting. Ensemble learning. Training sets Hypotheses Ensemble hypothesis S 1 h 1

706 views • 31 slides

Study on Ensemble Learning

Study on Ensemble Learning. By Feng Zhou. Content . Introduction A Statistical View of M3 Network Future Works. Introduction. Ensemble learning: To combine a group of classifiers rather than to design a new one.

349 views • 16 slides

Forest tree seed research

Forest tree seed research. Research Organisations specialising in above tend to be …. Independent Isolated from seed research more widely &amp; Timber Industry/Forest Environment focused. DIFFICULTIES WITH TREE SEED RESEARCH. Poor viability Considerable dormancy

552 views • 38 slides

Ensemble Learning: AdaBoost

Ensemble Learning: AdaBoost. Jianping Fan Dept of Computer Science UNC-Charlotte. Ensemble Learning. A machine learning paradigm where multiple learners are used to solve the problem. Previously: single classifier. Ensemble: multiple classifiers. Problem. Problem. …. Learner. ….

731 views • 63 slides

Sparse vs. Ensemble Approaches to Supervised Learning

Sparse vs. Ensemble Approaches to Supervised Learning. Greg Grudic Modified by Longin Jan Latecki Some slides are by Piyush Rai. Goal of Supervised Learning?. Minimize the probability of model prediction errors on future data Two Competing Methodologies Build one really good model

358 views • 34 slides

Ensemble Learning

519 views • 47 slides

Ensemble Learning: An Introduction

422 views • 30 slides

Ensemble learning

Ensemble learning. Basic idea.

603 views • 23 slides

Forest Glenn Tree Care Inc

If the trees at your Sutton West location leave much to be desired, then it's high time for a call to Forest Glenn Tree Care Inc. We're your local tree experts, capable of dealing with a veritable A-Z of common and more rare tree services, including removal, planting, pruning, trimming, cabling, bracing, and tree consultations. Get the exact tree service you need by dialing (416) 738-4536.

64 views • 4 slides

More Related