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Support Vector Machine For Ecg Beat Classification
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Support Vector Machine For Ecg Beat Classification

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this presentation gives an introduction to SVM and its idea. At the end an show the result of the SVM in ECG beat classification and comparison with Neural Networks

this presentation gives an introduction to SVM and its idea. At the end an show the result of the SVM in ECG beat classification and comparison with Neural Networks

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    Support Vector Machine For Ecg Beat Classification Support Vector Machine For Ecg Beat Classification Presentation Transcript

    • A.RahimK.Mohammadi
      Spring 2011
      Support Vector Machine for ECG Beat Classification
    • Maximum Margin
      Support Vector Machine (SVM)
      Multi-class SVM
      Result and Discussion
      Outline
    • Maximum Margin
      denotes +1
      denotes -1
      Any of these would be fine..
      ..but which is best?
    • Linear Classifiers
      denotes +1
      denotes -1
      How would you classify this data?
      Misclassified
      to +1 class
    • Maximum Margin
      SVM is a binary classification which separates classes in feature space
      The maximum margin linear classifier is the linear classifier with the maximum margin.
      This is the simplest kind of SVM (Called an LSVM)
      Support Vectors are those datapoints that the margin pushes up against
      Linear SVM
    • SVM
    • SVM
      Given the training sample
      and kernel function K
      SVM will find a coefficient ai for each xi through an quadratic maximization programming
      𝑖=1𝑛𝑎𝑖− 12𝑖,𝑗=1𝑛𝑎𝑖𝑎𝑗𝑦𝑖𝑦𝑗𝐾𝒙𝑖,𝒙𝑗𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 0≤𝑎𝑖≤𝐶,  𝑖=1,2,…,𝑛 𝑎𝑛𝑑 𝑖=1𝑛𝑎𝑖𝑦𝑖=0
      Wher C is The cost parameter 
      Every new pattern x is classified to either one of the two categories
       
      𝑓𝑥=𝑠𝑖𝑔𝑛𝑖=1𝑛𝑦𝑖𝑎𝑖𝐾𝒙,𝒙𝒊+𝑏
       
    • Non-linear SVMs: Feature spaces
      • General idea: the original input space can always be mapped to some higher-dimensional feature space where the training set is separable:
      Φ: x->φ(x)
    • One-against-all (OAA) SVMs
      Multi-class SVM
    • One-Against-One (OAO) SVMs
      Multi-class SVM
    • ECG beat Classification System
    • Results
    • Some Issues
      Choice of kernel
      - Gaussian or polynomial kernel is default
      - if ineffective, more elaborate kernels are needed
      - domain experts can give assistance in formulating appropriate similarity measures
      Choice of kernel parameters
      - e.g. σ in Gaussian kernel
      - σ is the distance between closest points with different classifications
      - In the absence of reliable criteria, applications rely on the use of a validation set or cross-validation to set such parameters.
      Optimization criterion – Hard margin v.s. Soft margin
      - a lengthy series of experiments in which various parameters are tested
    • SVMs are currently among the best performers for a number of classification tasks
      SVMs can be applied to complex data types beyond feature vectors (e.g. graphs, sequences, relational data) by designing kernel functions for such data.
      SVM successfully applied for multi-class classification
      The result shows the high performance of SVM in ECG beat classification
      SVM is good when we have high dimension feature space and lots of train patterns
      Conclusion
    • An excellent tutorial on VC-dimension and Support Vector Machines:
      C.J.C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):955-974, 1998.
      The VC/SRM/SVM Bible:
      Statistical Learning Theory by Vladimir Vapnik, Wiley-Interscience; 1998
      Some Resources
      http://www.kernel-machines.org/
    • Chih-Wei Hsu and Chih-Jen Lin (2002). "A Comparison of Methods for Multiclass Support Vector Machines". IEEE Transactions on Neural Networks
      http://www.iro.umontreal.ca/~pift6080/H09/documents/papers/svm_tutorial.ppt
      Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001
      Reference
    • Thank YouWelcome your comments and questions