Dominance detection in Meetings Using Support Vector Machines

Feb 01, 2013

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Outline. DominanceDominance JudgementsTowards automatic dominance detectionResultsConclusion. Cooperative. For a meeting to be effective, participants should be cooperativeThe Grice's cooperation principle is founded on four maxims [Grice,1975]Speak no more or less than requiredOnly say thi

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Dominance detection in Meetings Using Support Vector Machines

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Support Vector Machines

Support Vector Machines. Chapter 12. Separating Hyperplanes – Separable Case Extension to Non-separable case – SVM Nonlinear SVM SVM as a Penalization method SVM regression. Outline. Separating Hyperplanes.

995 views • 49 slides

Support Vector Machines

Support Vector Machines . Adapted from Lectures by Raymond Mooney (UT Austin) and Andrew Moore (CMU). Text classification. Earlier: Algorithms for text classification K Nearest Neighbor classification Simple, expensive at test time, low bias, high variance, non-linear

1.04k views • 64 slides

Support Vector Machines

Support Vector Machines. Hongning Wang CS@UVa. Today’s lecture. Support vector machines Max margin classifier Derivation of linear SVM Binary and multi-class cases Different types of losses in discriminative models Kernel method Non-linear SVM Popular implementations.

843 views • 54 slides

Support Vector Machines

Support Vector Machines. Perceptron Revisited:. y( x ) = sign( w . x + b ). Linear Classifier:. w . x + b > 0. w . x + b = 0. w . x + b < 0. Which one is the best?. d. Notion of Margin. Distance from a data point to the boundary:

414 views • 25 slides

Support Vector Machines

Support Vector Machines. Lecturer : Yishay Mansour Itay Kirshenbaum. Lecture Overview. In this lecture we present in detail one of the most theoretically well motivated and practically most eﬀective classiﬁcation algorithms in modern machine learning: Support Vector Machines (SVMs). .

666 views • 41 slides

Support Vector Machines

Support Vector Machines. Reading: Ben- Hur and Weston, “A User’s Guide to Support Vector Machines” (linked from class web page). Notation. Assume a binary classification problem: h ( x )  {−1, 1} Instances are represented by vector x   n .

830 views • 53 slides

Support Vector Machines

Support Vector Machines. Pattern Recognition Sergios Theodoridis Konstantinos Koutroumbas Second Edition A Tutorial on Support Vector Machines for Pattern Recognition Data Mining and Knowledge Discovery, 1998 C. J. C. Burges. Separable Case. Maximum Margin Formulation. Separable Case.

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Support Vector Machines

Support Vector Machines . More information can be found in http://www.cs.cmu.edu/~awm/tutorials. a. Linear Classifiers. x. f . y est. f ( x , w ,b ) = sign( w . x - b ). denotes +1 denotes -1. How would you classify this data?. a. Linear Classifiers. x. f . y est.

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Support Vector Machines

Support Vector Machines. Modified from Prof. Andrew W. Moore’s Lecture Notes www.cs.cmu.edu/~awm. History. SVM is a classifier derived from statistical learning theory by Vapnik and Chervonenkis SVMs introduced by Boser, Guyon, Vapnik in COLT-92

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Support Vector Machines

Support Vector Machines. Presented By Sherwin Shaidaee. Papers. Vladimir N. Vapnik, “The Statistical Learning Theory”. Springer, 1998 Yunqiang Chen, Xiang Zhou, and Thomas S. Huang, University of Illinois, “ONE-CLASS SVM FOR LEARNING IN IMAGE RETRIEVAL”, 2001

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Support Vector Machines

Support Vector Machines. Presented by: Yasmin Anwar. Outlines. Introduction Support Vector Machines SVM Tools SVM Applications Text Classification Conclusion. Introduction. Proposed by Boser, Guyon and Vapnik in 1992. SVMs are one of the best classification methods.

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Support Vector Machines

Support Vector Machines. SVMs. Feature Space. Separable?. Kernels and Support Vectors. Calculation. Higgs Parameters. Higgs Parameters. Higgs Output. Higgs Purity / Efficiency. Kernel Width. L2 b Tagger Parameters. L2 b Tagger Parameters. L2 b Tagger Output. SVR. NN.

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Speaker Change Detection using Support Vector Machines

Speaker Change Detection using Support Vector Machines. V.Kartik, D.Srikrishna Satish and C.Chandra Sekhar Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Madras, Chennai – India. Speaker Change Detection.

268 views • 11 slides

Support Vector Machines

Summer Course: Data Mining. Support Vector Machines. Support Vector Machines and other penalization classifiers. Presenter: Georgi Nalbantov. Presenter: Georgi Nalbantov. August 2009. Contents. Purpose Linear Support Vector Machines Nonlinear Support Vector Machines

748 views • 52 slides

Support Vector Machines

Support Vector Machines. 1. Introduction to SVMs 2. Linear SVMs 3. Non-linear SVMs. References: 1. S.Y. Kung, M.W. Mak, and S.H. Lin. Biometric Authentication: A Machine Learning Approach, Prentice Hall, to appear.

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Dominance detection in Meetings Using Support Vector Machines

Dominance detection in Meetings Using Support Vector Machines. Rutger Rienks and Dirk Heylen University of Twente MLMI 2005. Outline. Dominance Dominance Judgements Towards automatic dominance detection Results Conclusion. Cooperative.

194 views • 18 slides

Support Vector Machines

575 views • 45 slides

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