'Weak learner' presentation slideshows

boosting

836 views • 65 slides

Boosting

Boosting. Shai Raffaeli Seminar in mathematical biology. http://www1.cs.columbia.edu/~freund/. Male. Human Voice. Female. Toy Example. Computer receives telephone call Measures Pitch of voice Decides gender of caller. mean1. mean2. var1. var2. Probability. Generative modeling.

756 views • 64 slides

Boosting for prediction and inference

Boosting for prediction and inference. By Marc Sobel. Sometimes you just need a boost!!!. Learning.

707 views • 56 slides

Machine Learning

Machine Learning. Ensemble Models. ? ? ? ? ? ? ? ?. Training Data. Data1. Data m. Data2. ? ? ? ? ? ? ? ?. Learner m. Learner2. Learner1. ? ? ? ? ? ? ? ?. Model1. Model2. Model m. Final Model. Model Combiner. Learning Ensembles.

554 views • 55 slides

Chapter 7: Ensemble Methods

Chapter 7: Ensemble Methods. Ensemble Methods. Rationale Combining classifiers Bagging Boosting Ada-Boosting. Rationale. In any application, we can use several learning algorithms; hyperparameters affect the final learner

721 views • 54 slides

Processing of large document collections

Processing of large document collections. Part 4 (Information gain, boosting, text summarization) Helena Ahonen-Myka Spring 2005. In this part. Term selection: information gain Boosting Text summarization. Term selection: information gain.

575 views • 49 slides

Adaboost

765 views • 46 slides

Adaboost

641 views • 46 slides

My JSM Presentation

Dick De Veaux. Williams College. My JSM Presentation. Note to self: Basically the same talk I gave somewhere else where I had about 10 times longer to give it. I hope it goes better this time where I only have 15 minutes.

528 views • 45 slides

Web Taxonomy Integration through Co-Bootstrapping

520 views • 41 slides

Adam Tauman Kalai, Georgia Tech. Yishay Mansour, Google and Tel-Aviv Elad Verbin, Tsinghua

On Agnostic Boosting and Parity Learning. Adam Tauman Kalai, Georgia Tech. Yishay Mansour, Google and Tel-Aviv Elad Verbin, Tsinghua. Defs. Agnostic Learning = learning with adversarial noise Boosting = turn weak learner into strong learner Parities = parities of subsets of the bits

814 views • 39 slides

Presenter: Derek Hoiem CS 598, Spring 2009 Feb 24, 2009

Statistical Template-Based Object Detection A Statistical Method for 3D Object Detection Applied to Faces and Cars Henry Schneiderman and Takeo Kanade Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola and Michael Jones. Presenter: Derek Hoiem CS 598, Spring 2009

577 views • 39 slides

Adam Tauman Kalai, Georgia Tech. Yishay Mansour, Google and Tel-Aviv Elad Verbin, Tsinghua

On Agnostic Boosting and Parity Learning. Adam Tauman Kalai, Georgia Tech. Yishay Mansour, Google and Tel-Aviv Elad Verbin, Tsinghua. Defs. Agnostic Learning = learning with adversarial noise Boosting = turn weak learner into strong learner Parities = parities of subsets of the bits

474 views • 39 slides

Learning to Rank

Learning to Rank. Ming-Feng Tsai National Taiwan University. Ranking. Ranking vs. Classification Training samples is not independent, identical distributed. Criterion of training is not compatible to one of IR Many ML approaches have been applied to ranking RankSVM

440 views • 28 slides

An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform

An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform. Behrad Bagheri. Linxia Liao. About the Author. Linxia Liao B. Sc. In Mechanical Science & Engineering, 2001, HUST, Wuhn , China M. Sc. Mechanical Science & Engineering, 2004, Huazhong University of S&T.

438 views • 25 slides

The Viola/Jones Face Detector

428 views • 17 slides

CSSE463: Image Recognition Day 31

249 views • 11 slides

CSSE463: Image Recognition Day 27

179 views • 10 slides

150 views • 1 slides

Training Conditional Random Fields using Virtual Evidence Boosting

83 views • 1 slides