'Kernel trick' diaporamas de présentation

Topic Classification in R

Topic Classification in R. A Tutorial on Using Text Mining and Machine Learning Technologies to Classify Documents. December 2009. johannes.liegl@gmail.com. Thanks. Marco Maier (PhD Student at WU Wien) Answering all of my R-related questions Endless Patience

1.04k views • 84 slides

Clustering Techniques and Applications to Image Segmentation

Clustering Techniques and Applications to Image Segmentation. Liang Shan shan@cs.unc.edu. Roadmap. Unsupervised learning Clustering categories Clustering algorithms K-means Fuzzy c-means Kernel-based Graph-based Q&A. Unsupervised learning. Definition 1

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Support Vector Machines and Kernel Methods

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Support Vector Machines

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Classification IV

Classification IV. Lecturer: Dr. Bo Yuan E-mail: yuanb@sz.tsinghua.edu.cn. Overview. Support Vector Machines. Linear Classifier. w. w ? x + b =0. w ? x + b <0. w ? x + b >0. Distance to Hyperplane. x. x '. Selection of Classifiers. ?. Which classifier is the best?

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Pattern Recognition and Machine Learning

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Lecture 5 Support Vector Machines

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Support Vector Machines for Structured Classification and The Kernel Trick

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CSC321: Lecture 27: Support Vector Machines

CSC321: Lecture 27: Support Vector Machines. Geoffrey Hinton. Another way to choose a model class. We want to get a low error rate on unseen data. It would be really helpful if we could get a guarantee of the following form: Test error rate =< train error rate + f(N, h, p)

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Kernel-class

Kernel-class. Jan. 13 2005. Recap : Feature Spaces. non-linear mapping to F 1. high-D space 2. infinite-D countable space : 3. function space (Hilbert space). example:. Recap : Kernel Trick. Note: In the dual representation we used the Gram matrix to express the solution.

176 views • 9 slides