'Empirical risk' presentation slideshows

Support Vector Machines and Radial Basis Function Networks

Support Vector Machines and Radial Basis Function Networks. Instructor: Tai-Yue (Jason) Wang Department of Industrial and Information Management Institute of Information Management. Statistical Learning Theory. Learning from Examples. Learning from examples is natural in human beings

2.25k views • 194 slides

Support Vector Machines and Radial Basis Function Networks

Support Vector Machines and Radial Basis Function Networks. Instructor: Tai-Yue (Jason) Wang Department of Industrial and Information Management Institute of Information Management. Statistical Learning Theory. Learning from Examples. Learning from examples is natural in human beings

2.16k views • 194 slides

Learning with Trees

Learning with Trees. Rob Nowak. University of Wisconsin-Madison Collaborators: Rui Castro, Clay Scott, Rebecca Willett. www.ece.wisc.edu/~nowak. Artwork: Piet Mondrian. Basic Problem: Partitioning.

958 views • 87 slides

Support Vector Machines

Support Vector Machines. ???????. Content. Introduction The VC Dimension & Structure Risk Minimization Linear SVM ? The Separable case Linear SVM ? The Non-Separable case Lagrange Multipliers. Support Vector Machines. Introduction. Learning Machines. A machine to learn the mapping

1.13k views • 74 slides

Lecture 3: Linear Regression

Lecture 3: Linear Regression. Machine Learning CUNY Graduate Center. Today. Calculus Lagrange Multipliers Linear Regression. Optimization with constraints. What if I want to constrain the parameters of the model. The mean is less than 10

725 views • 62 slides

Methods of Pattern Recognition chapter 5 of: Statistical learning methods by Vapnik

Methods of Pattern Recognition chapter 5 of: Statistical learning methods by Vapnik. Zahra Zojaji. Introduction. To implement the SRM inductive principle in learning algorithms one has to minimize the risk in a given set of functions by controlling two factors: empirical risk

821 views • 62 slides

885 views • 60 slides

Statistical Learning Theory and Classification based on Support Vector Machines

Presentation by Michael Sullivan. Based on: The Nature of Statistical Learning Theory by V. Vapnick 2009 Presentation by John DiMona and some slides based on lectures given by Professor Andrew Moore of Carnegie Mellon University.

809 views • 59 slides

667 views • 50 slides

659 views • 50 slides

MITM 613 Intelligent System

MITM 613 Intelligent System. Chapter 8a: Support Vector Machine. Chapter Eight(a) : SVM. Introduction Theory Implementation Tools Comparison LIBSVM practical. Introduction (1 of 9). Introduction (1). SVM is mainly used in the problem of classification and regression .

557 views • 42 slides

CS 570 Artificial Intelligence Introduction to Statistical Learning Theory

516 views • 27 slides

Survey ICASSP 2007 Discriminative Training

Survey ICASSP 2007 Discriminative Training. Reporter: Shih-Hung Liu 2007/04/30. References. Large-Margin Minimum Classification Error Training for Large-Scale Speech Recognition Tasks Dong Yu, Li, Deng Xiaodong He, Alex Acero , Microsoft

320 views • 22 slides

Support Vector classifiers for Land Cover Classification

Support Vector classifiers for Land Cover Classification Mahesh Pal Paul M. Mather National Institute of tecnology School of geography

308 views • 21 slides

Regularized risk minimization

Regularized risk minimization. Usman Roshan. Supervised learning for two classes. We are given n training samples (x i ,y i ) for i=1..n drawn i.i.d from a probability distribution P(x,y). Each x i is a d-dimensional vector ( x i in R d ) and y i is +1 or -1

397 views • 21 slides

CMS 165 Lecture 8

CMS 165 Lecture 8. Approximation and Generalization in Neural Networks. Recall from previous lecture: Hypothesis class: A loss function: Expected risk: Expected risk minimizer: Given a set of samples: Empirical risk:

178 views • 17 slides

1. Stat 231. A.L. Yuille. Fall 2004

1. Stat 231. A.L. Yuille. Fall 2004. PAC Learning and Generalizability . Margin Errors. Structural Risk Minimization. 2. Induction: History. Francis Bacon described empiricism. Formulate hypotheses and test by experiments. English Empiricist School of Philosophy.

342 views • 16 slides

Decision Trees

Decision Trees. Prof. A.L. Yuille. Stat 231. Fall 2004. Duda, Hart & Stork. Chp 81-8.3 . Decision Trees. Decision Trees. Binary Classification Trees. Data can be non-metric, list of binary attributes. 2. Classification Decision Trees. Nonmetric. Data is list of binary attributes.

785 views • 15 slides

Goal of Learning Algorithms

Goal of Learning Algorithms. The early learning algorithms were designed to find such an accurate fit to the data. A classifier is said to be consistent if it performed the correct classification of the training data. The ability of a classifier to correctly classify data not

174 views • 11 slides