Linear Classifiers

Feb 11, 2013

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Linear Classifiers

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Classifiers

Classifiers. American Sign Language III. Descriptive Classifiers. Used when describing the physical characteristics of nouns. Example:. Example:. CL:55(bent) - sphere. Example:. Example:. CL:LL(bent) – thin circular object. Example:. Example:. CL:CC SS – narrowing object. Example:.

819 views • 54 slides

Classifiers

Classifiers. Given a feature representation for images, how do we learn a model for distinguishing features from different classes?. Decision boundary. Zebra. Non-zebra. Classifiers.

565 views • 30 slides

Linear Classifiers

Linear Classifiers. Dept. Computer Science & Engineering, Shanghai Jiao Tong University. Outline. Linear Regression Linear and Quadratic Discriminant Functions Reduced Rank Linear Discriminant Analysis Logistic Regression Separating Hyperplanes. Linear Regression.

740 views • 33 slides

Classifiers

Classifiers. ASL II. What is a classifier?. Represents a noun and indicates the location and possible action of that noun. Size and shape specifiers (SASSes) Indicates something about the size, shape, texture, etc. and location of a noun. Example: (pronoun). The red car pulled up behind me.

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Classifiers

Classifiers. Basic Description / Beginning Storytelling. Telling Simple Stories Using Classifiers. (SW Group 1 Handshapes). Handshape or Classifier? Classifier – a sign that represents classes of objects.

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Classifiers

Classifiers. What is a Classifier?. CL stands for a particular group or ‘class' of nouns. She = woman, waitress, mom, etc…(NOT man, waiter, dad) In the same way CL:3 = Vehicle (bus, car, boat, etc…. Several Important things about CLs. They show how classifiers function as pronouns

1k views • 41 slides

Linear Classifiers/SVMs

Linear Classifiers/SVMs. David Kauchak, CS311, Spring 2013. Admin. Midterm exam posted Assignment 4 due Friday by 6pm No office hours tomorrow. Math. Machine learning often involves a lot of math some aspects of AI also involve some familiarity Don’t let this be daunting

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Non Linear Classifiers

Non Linear Classifiers. The XOR problem. There is no single line (hyperplane) that separates class A from class B. On the contrary, AND and OR operations are linearly separable problems. The Two-Layer Perceptron For the XOR problem, draw two , instead, of one lines.

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Classifiers

04/05/12. Classifiers. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Today’s class. Review of image categorization Classification A few examples of classifiers: nearest neighbor, generative classifiers, logistic regression, SVM

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Linear hyperplanes as classifiers

Linear hyperplanes as classifiers. Usman Roshan. Hyperplane separators. Hyperplane separators. w. Hyperplane separators. w. Hyperplane separators. r. x p. x. w. Hyperplane separators. r. x p. x. w. Nearest mean as hyperplane separator. m 2. m 1.

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“Classifiers”

“Classifiers”. Under the guidance of Prof. Pushpak Bhattacharyya pushpakbh@gmail.com IIT Bombay. R & D project by Aditya M Joshi adityaj@cse.iitb.ac.in IIT Bombay. Overview. Introduction to Classification. What is classification?.

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Classification and Linear Classifiers

In the Name of God. Machine Learning. Classification and Linear Classifiers. Mohammad Ali Keyvanrad. Thanks to: M . Soleymani (Sharif University of Technology ) R. Zemel (University of Toronto ) p. Smyth (University of California, Irvine). Fall 1392. Outline. Classification

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On Optimal Pairwise Linear Classifiers for Normal Distributions

B. John Oommen A Joint Work with Luis G. Rueda School of Computer Science Carleton University. On Optimal Pairwise Linear Classifiers for Normal Distributions. Problem: Find a Linear Classifier (also Optimal ) For normal distributions: Believed linear only when covariance matrices equal

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Classifiers!!!

Classifiers!!!. BCH394P/364C Systems Biology / Bioinformatics. Edward Marcotte, Univ of Texas at Austin.

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Linear Classifiers

Linear Classifiers. Based on slides by William Cohen, Andrej Karpathy, Piyush Rai. Linear Classifiers. Let’s simplify life by assuming: Every instance is a vector of real numbers, x =( x 1 ,…,x n ). ( Notation: boldface x is a vector.)

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Linear classifiers

Linear classifiers. Introduction. Remember that Bayes’ decision rule requires the knowledge of either P(C i |X) or P(X|C i ) and P(C i ) We showed how to create an estimate for P(X|C i ) using one Gaussian curve or a mixture of Gaussians

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Classifiers

Classifiers . Fujinaga. Bayes (optimal) Classifier (1). A priori probabilities: and Decision rule: given and decide if and probability of error Let be the feature(s).

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Non Linear Classifiers

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