'Hypothesis h' presentation slideshows

Basic Bayes: model fitting, model selection, model averaging

969 views • 56 slides

Bayesian Learning

Bayesian Learning. Bayesianism vs. Frequentism. Classical probability: Frequentists Probability of a particular event is defined relative to its frequency in a sample space of events.

662 views • 54 slides

Learning from Observations

626 views • 53 slides

578 views • 47 slides

Introduction to Probability Theory

765 views • 39 slides

BOOSTING & ADABOOST

BOOSTING & ADABOOST. Lecturer: Yishay Mansour Itay Dangoor. Overview. Introduction to weak classifiers Boosting the confidence Equivalence of weak & strong learning Boosting the accuracy - recursive construction AdaBoost. Weak Vs. Strong Classifiers. PAC (Strong) classifier

766 views • 38 slides

Chapter 17 Advanced Knowledge Representation

Chapter 17 Advanced Knowledge Representation. Chapter 17 Contents (1). The Blackboard Architecture Scripts The Copycat Architecture Nonmonotonic Reasoning The Modal Operator M Default Reasoning Truth Maintenance Systems Closed-World Assumption. Chapter 17 Contents (2). Circumscription

440 views • 30 slides

Adaboost Theory

Adaboost Theory. Exponential loss function of a classifier h : Suppose we have run Adaboost for T iterations. We have our ensemble classifier H T . Now suppose we run it for one additional iteration. Then we have a classifier H T +? T+ 1 h T+ 1 .

341 views • 10 slides

Steps of The Scientific Method:

252 views • 10 slides