'Linear discriminant' presentation slideshows

Information Retrieval as Structured Prediction

Information Retrieval as Structured Prediction. University of Massachusetts Amherst Machine Learning Seminar April 29 th , 2009 Yisong Yue Cornell University Joint work with: Thorsten Joachims, Filip Radlinski, and Thomas Finley. Supervised Learning. x. y. 7.3. x. x. y. y. 1. -1.

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Perceptrons

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Discriminative, Unsupervised, Convex Learning

Discriminative, Unsupervised, Convex Learning. Dale Schuurmans Department of Computing Science University of Alberta MITACS Workshop, August 26, 2005. Current Research Group. PhD Tao Wang reinforcement learning PhD Ali Ghodsi dimensionality reduction

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Unsupervised and Transfer Learning Challenge

IJCNN 2011 San Jose, California Jul. 31, Aug. 5, 2011. Unsupervised and Transfer Learning Challenge. Isabelle Guyon Clopinet, California. Credits. Data donors:

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Perceptrons

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Reduksi Dimensi Image dengan Principal Components Analysis (PCA)

Reduksi Dimensi Image dengan Principal Components Analysis (PCA) . Sumber: Trucco & Verri chap. 10 Standford Vision & Modeling. Contoh: problem Pattern Recognition. Rotate coordinate system:. Problem Dimensi tinggi ??. PCA (Principal Component Analysis).

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Classification: Linear Models

Classification: Linear Models. Prof. Navneet Goyal CS & IS BITS, Pilani. Classification. By now, you are well aware of the classification problem Assign an input vector x to one of the K discrete disjoint classes, C k

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Generally Discriminant Analysis

Generally Discriminant Analysis. ?? : ??? ?? : 2004/10/29. Outline. Introduction Classification Feature Space Transformation Criterion. Introduction. Discriminant Analysis discriminant (classification, predictor) ??? class ???? , ????????? , ????????????? , ?????????????

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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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Multivariate Analysis Past, Present and Future

Multivariate Analysis Past, Present and Future. Harrison B. Prosper Florida State University PHYSTAT 2003 10 September 2003. Outline. Introduction Historical Note Current Practice Issues Summary. Introduction. Data are invariably multivariate Particle physics ( h , f , E, f)

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Part 3: Estimation of Parameters

Part 3: Estimation of Parameters. Estimation of Parameters. Most of the time, we have random samples but not the densities given. If the parametric form of the densities are given or assumed, then, using the labeled samples, the parameters can be estimated. (supervised learning)

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Multivariate Analysis A Unified Perspective

Multivariate Analysis A Unified Perspective. Harrison B. Prosper Florida State University Advanced Statistical Techniques in Particle Physics Durham, UK, 20 March 2002. Outline. Introduction Some Multivariate Methods Fisher Linear Discriminant (FLD) Principal Component Analysis (PCA)

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Multivariate Analysis A Unified Perspective

Multivariate Analysis A Unified Perspective. Harrison B. Prosper Florida State University Advanced Statistical Techniques in Particle Physics Durham, UK, 20 March 2002. Outline. Introduction Some Multivariate Methods Fisher Linear Discriminant (FLD) Principal Component Analysis (PCA)

340 views • 23 slides

Non-Bayes classifiers.

Non-Bayes classifiers. Linear discriminants, neural networks. Discriminant functions(1). Bayes classification rule:. Instead might try to find a function:. is called discriminant function. - decision surface. Class 1. Class 1. Class 2. Class 2. Discriminant functions (2).

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Fast and accurate text classification via multiple linear discriminant projections

Fast and accurate text classification via multiple linear discriminant projections. Soumen Chakrabarti Shourya Roy Mahesh Soundalgekar IIT Bombay www.cse.iitb.ac.in/~soumen. Introduction. Supervised learning of labels from high-dimensional data has many applications

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COMP/EECE 7/8740 Neural Networks

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Robust Fisher Discriminant Analysis

Robust Fisher Discriminant Analysis. Article presented at NIPS 2005 By Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd Presenter: Erick Delage February 14, 2006. Outline. Background on Fisher Linear Discriminant Analysis

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13. Component Analysis (also Chap 3)

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. 13. Component Analysis (also Chap 3).

258 views • 5 slides