'Support vectors' diaporamas de présentation

SVMs: Linear and Beyond

SVMs: Linear and Beyond. Advanced Statistical Methods in NLP Ling 572 February 21, 2012. SVM Training. Goal: Maximum margin, consistent w/training data. Fig. from F. Xia. SVM Training. Goal: Maximum margin, consistent w/training data Margin = 2d = 2/||w||. Fig. from F. Xia. SVM Training.

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Linear Classifiers/SVMs

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Wednesday, 20 February 2008 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 13 of 42. Support Vector Machines (concluded) and Association Rule Mining. Wednesday, 20 February 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org/Courses/Spring-2008/CIS732/ Readings:

762 views • 65 slides

Bayesian Learning for Conditional Models

Bayesian Learning for Conditional Models. Alan Qi MIT CSAIL September, 2005 Joint work with T. Minka, Z. Ghahramani, M. Szummer, and R. W. Picard. Motivation. Two types of graphical models: generative and conditional Conditional models: Make no assumptions about data generation

723 views • 50 slides

Support Vector Machines

967 views • 49 slides

Music Classification Using SVM

Music Classification Using SVM. Ming-jen Wang Chia-Jiu Wang. Outline. Introduction Support Vector Machine (SVM) Implementation with SVM Results Comparison with other algorithms Conclusion. Music Genre Classification. Human can identify music genre easily. (play clips)

815 views • 43 slides

Object recognition

Object recognition. Methods for classification and image representation. Credits. Slides by Pete Barnum Slides by Fei-Fei Li Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04 Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

679 views • 42 slides

Object recognition

Object recognition. Methods for classification and image representation. Credits. Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04 Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

782 views • 42 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 e?ective classi?cation algorithms in modern machine learning: Support Vector Machines (SVMs). .

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Discriminative and generative methods for bags of features

Discriminative and generative methods for bags of features. Zebra. Non-zebra. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. Image classification.

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Support Vector Machines and The Kernel Trick

Support Vector Machines and The Kernel Trick. William Cohen 3-26-2007. ^. ^. If mistake: v k+1 = v k + y i x i. Compute: y i = v k . x i. y i. y i. The voted perceptron. instance x i. B. A. v 2. (3a) The guess v 2 after the two positive examples: v 2 = v 1 + x 2.

823 views • 31 slides

Support vector machine

Support vector machine. Nonparametric Supervised Learning. Outline. Context of the Support Vector Machine Intuition Functional and Geometric Margins Optimal Margin Classifier Linearly Separable Not Linearly Separable Kernel Trick Aside: Lagrange Duality Summary.

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Support Vector Machine (SVM)

Support Vector Machine (SVM). Based on Nello Cristianini presentation http:// www.support-vector.net/tutorial.html. Basic Idea. Use Linear Learning Machine (LLM). Overcome the linearity constraints: Map to non-linearly to higher dimension. Select between hyperplans Use margin as a test

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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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578 views • 23 slides

Separating Hyperplanes

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Support Vector Machines: Brief Overview

Support Vector Machines: Brief Overview. Outline. Microarray Example Support Vector Machines (SVMs) Software: libsvm A Baseball Example with libsvm. Classifying Cancer Tissue: The ALL/AML Dataset.

208 views • 20 slides

Trading Convexity for Scalability

Marco A. Alvarez CS7680 Department of Computer Science Utah State University. Trading Convexity for Scalability. Paper.

343 views • 18 slides

Chapter 6. Classification and Prediction

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Final Review

Final Review. 7-Text Mining. Unstructured Data Two modes of mining: analysis vs. retrieval Precision vs. Recall as metric With lots of data you can find anything Tools for text mining Stopwords, stemming Term document matrix TF-IDF Latent Semantic Indexing (LSI)

173 views • 8 slides