Upgrade to Pro — share decks privately, control downloads, hide ads and more …

SVM classifiers

vhqviet
February 26, 2018
120

SVM classifiers

vhqviet

February 26, 2018
Tweet

Transcript

  1. • Input: Given training data (x i , y i

    ) for i = 1 ...N, with x i and y ∈ i {−1, 1} ∈ • Task: learn a classifier f(x) such that 2 Linear Classifiers
  2. A linear classifier has the form 4 Linear Classifiers •

    w is the normal to the line, and b the bias • w is known as the weight vector
  3. Linear Support Vector Machine ( LSVM) • SVM can be

    formulated as an optimization: • Or equivalently:
  4. 9 Linear Support Vector Machine ( LSVM) • Change to

    the Lagrange formulation: The Maximization depends only on dot products of pairs of vectors Maximize
  5. Not Linearly Separable Data Not Linearly Separable Data transform to

    a higher demensional space, called feature space. 11 Support Vector Machine ( SVM) kernel Maximization depends only on dot products
  6. Multiple Classes 14 Support Vector Machine ( SVM) • There

    are 2 kind of comparision for doing multiple classes >> .SVC(kernel='linear', decision_function_shape='ovr’) OVO: One vs One OVR: One vs Rest Pros: less sensitive to imbalanced Cons: More classifications Pros: Fewer classifications Cons: Classes may be imbalanced
  7. 15 • Pros: • Good at dealing with high dimentional

    data. • Works well on small data sets. • Different kernel functions for various decision functions or combine 2 different kernel functions for better result. • Cons: • Picking the right kernel and parameters can be computationally intensive. • SVM do not provide probability estimates. Support Vector Machine ( SVM)
  8. 参考文献 16 • Support Vector Machines - Patrick Winston, MIT

    OpenCourseWare • Understanding Support Vector Machine algorithm from examples - Sunil Ray