'Decision boundary' diaporamas de présentation

Support Vector Machines

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Lecture 3: Introduction to Classification

Lecture 3: Introduction to Classification. CS 175, Fall 2007 Padhraic Smyth Department of Computer Science University of California, Irvine. Outline. Overview of Classification: examples and applications of classification classification: mapping from features to a class label

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Chapter 5: Linear Discriminant Functions (Sections 5.1-5.3, 5.4, 5.11)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 5: Linear Discriminant Functions (Sections 5.1-5.3, 5.4, 5.11).

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From Biological to Artificial Neural Networks

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Evolutionary Computation For Backgammon david gabai

Evolutionary Computation For Backgammon david gabai. Articles to be reviewed. Why co-Evolution beats Temporal Difference learning in Backgammon for a linear architecture, but not a non-linear architecture

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Part 2: Support Vector Machines

Part 2: Support Vector Machines. Vladimir Cherkassky University of Minnesota cherk001@umn.edu Presented at Tech Tune Ups, ECE Dept, June 1, 2011. Electrical and Computer Engineering. 1. 1. SVM: Brief History. Margin (Vapnik & Lerner ) Margin (Vapnik and Chervonenkis, 1964)

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The Perceptron

The Perceptron. Neural Networks 3. Truth Table for Logical AND. x. y. x & y. 0. 0 . 0. 1 . 1 . 0. 1 . 1 . output. inputs. Prehistory.

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Linear Discriminant Analysis (Part II)

Linear Discriminant Analysis (Part II). Lucian, Joy, Jie. Questions - Part I. Paul:

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CS 4700: Foundations of Artificial Intelligence

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Automated Face Detection

Automated Face Detection. Peter Brende David Black-Schaffer Veni Bourakov. Primary Challenges. Scale differences Overlapping/obstructed faces Lighting variation. Implementation Overview . Color-based skin separation Spatial analysis to generate candidate faces Morphological?

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Part 1: Classical Image Classification Methods

Part 1: Classical Image Classification Methods. Kai Yu Dept. of Media Analytics NEC Laboratories America. Andrew Ng Computer Science Dept. Stanford University. Outline of Part 2. Local Features, Sampling, Visual Words Discriminative Methods Bag-of-Words (BoW) representation

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Separating Hyperplanes

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CSC321: Introduction to Neural Networks and Machine Learning Lecture 15: Mixtures of Experts

CSC321: Introduction to Neural Networks and Machine Learning Lecture 15: Mixtures of Experts . Geoffrey Hinton. Very local models e.g. Nearest neighbors Very fast to fit Just store training cases Local smoothing obviously improves things. Fully global models e. g. Polynomial

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Basic Classification

Basic Classification. Which is that?. The Classification Problem. On the basis of some examples, determine in which class a previously unobserved instance belongs Can be analogous to learning Supervised : a teacher prescribes class composition

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Classification Discriminant Analysis

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Predicting Depression Occurrence Using Classification Algorithm in Data Mining

Predicting Depression Occurrence Using Classification Algorithm in Data Mining. Abdur Rahman Department of Statistics Shahjalal University of Science and Technology Sylhet , Bangladesh E-mail: airdipu@gmail.com. Introduction. Universal definition of old age is elusive

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Linear Discriminant Functions

Linear Discriminant Functions. Chapter 3, pp. 77 - . Linear Discriminant Functions. Chapter 1 introduced the concept of a discriminant function y( x ) The vector x is assigned to class C 1 if y( x )>0 and C 2 if y( x )<0.

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