'Linear svm' presentation slideshows

1.13k views • 96 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

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Linear SVM

Linear SVM. From perceptron to kernel methods. Following Scholkopf, see SVM site. Course website: http://horn.tau.ac.il/course06.html. Support Vector Clustering. Given points x in data space, define images in Hilbert space.

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Computational Learning Theory and Kernel Methods

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LPP-HOG: A New Local Image Descriptor for Fast Human Detection

Andy {andy@ulsan.islab.ac.kr}. LPP-HOG: A New Local Image Descriptor for Fast Human Detection. Qing Jun Wang and Ru Bo Zhang IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, 2008. pp.640-643 21-22 Dec. 2008, Wuhan. Problem setting.

672 views • 41 slides

LPP-HOG: A New Local Image Descriptor for Fast Human Detection

Andy {andy@ulsan.islab.ac.kr}. LPP-HOG: A New Local Image Descriptor for Fast Human Detection. Qing Jun Wang and Ru Bo Zhang IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, 2008. pp.640-643 21-22 Dec. 2008, Wuhan. Problem setting.

545 views • 41 slides

Classification of Drugs by SVM

CZ3253: Computer Aided Drug design Lecture 7: Drug Design Methods II: SVM Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore. Classification of Drugs by SVM.

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Latent SVMs for Human Detection with a Locally Affine Deformation Field

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Support Vector Machine & Its Applications

Support Vector Machine & Its Applications. Overview. Intro. to Support Vector Machines (SVM) Properties of SVM Applications Gene Expression Data Classification Text Categorization if time permits Discussion. a. Linear Classifiers. x. f. y est. f ( x , w ,b ) = sign( w x + b ).

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Support Vector Machine & Its Applications

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Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 25 Kernel methods Source: W?odzis?aw Duch ; Dept. of Informatics, UMK ; Google: W Duch. Kernels!.

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CSCE822 Data Mining and Warehousing

CSCE822 Data Mining and Warehousing. Lecture 12 Support Vector Machines Dr. Jianjun Hu mleg.cse.sc.edu/edu/csce822. University of South Carolina Department of Computer Science and Engineering. Overview. Intro. to Support Vector Machines (SVM) Properties of SVM Applications

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Support Vector Machines in Marketing

Support Vector Machines in Marketing. Georgi Nalbantov. MICC, Maastricht University. Contents. Purpose Linear Support Vector Machines Nonlinear Support Vector Machines (Theoretical justifications of SVM) Marketing Examples Conclusion and Q & A (some extensions). Purpose.

477 views • 32 slides

CSE 881: Data Mining

CSE 881: Data Mining. Lecture 10: Support Vector Machines. Find a linear hyperplane (decision boundary) that will separate the data. Support Vector Machines. One Possible Solution. Support Vector Machines. Another possible solution. Support Vector Machines. Other possible solutions.

450 views • 25 slides

Lecture 31: Modern object recognition

CS4670 / 5670: Computer Vision. Noah Snavely. Lecture 31: Modern object recognition. Announcements. Project 4 due today Demos Monday. Object detection: where are we?. Incredible progress in the last ten years Better features, better models, better learning methods, better datasets

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An Introduction to Support Vector Machine Classification

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Lecture 31: Modern recognition

CS4670 / 5670: Computer Vision. Noah Snavely. Lecture 31: Modern recognition. Object detection: where are we?. Incredible progress in the last ten years Better features, better models, better learning methods, better datasets Combination of science and hacks.

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Using IR techniques to improve Automated Text Classification

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Weka solution for the 2004 KDD Cup Protein Homology Prediction task

Weka solution for the 2004 KDD Cup Protein Homology Prediction task. Bernhard Pfahringer Weka Group, University of Waikato, New Zealand. The problem. Detect homologous protein sequences 153 train sequences * ~1000 sequences ==> 145751 pairs classified as match or not

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CISC 841 Bioinformatics (Fall 2008) Review Session

CISC 841 Bioinformatics (Fall 2008) Review Session. Basics of Molecular biology. Central dogma Transcription Translation Genetic code DNA Double helix Watson-Crick binding RNA Secondary structures Genes Compositional Structure Reading frames Proteins

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