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Characterization of Kernels

Consider a finite space. is a symmetric function on. and. Characterization of Kernels. Motivation in Finite Input Space. Let. be a matrix defined as following:. There is an orthogonal matrix. such that:. Characterization of Kernels. Be Positive Semi-definite. Assume:. Let. where.

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Characterization of Kernels

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  1. Consider afinitespace . is a symmetric function on and Characterization of Kernels Motivation in Finite Input Space Let be a matrix defined as following: There is an orthogonal matrix such that:

  2. Characterization of Kernels Be Positive Semi-definite Assume: Let where

  3. be afinitespace Let . the corresponding matrix is Any finite subset of Mercer’s Conditions: Guarantee the Existence of Feature Space is a symmetric function on and Then is a kernel function if and only if is positive semi-definite. is infinite (but compact)? • What if Mercer’s conditions: positive semi-definite.

  4. Making Kernels Kernels Satisfy a Number of Closure Properties Let be kernels over be a kernel over and be a symmetric positive semi-definite. Then the following functions are kernels:

  5. The kernels are in the form: Translation Invariant Kernels • The inner product (in the feature space) of two inputs is unchanged if both are translated by the same vector. • Some examples: • Gaussian RBF: • Multiquadric: • Fourier:see Example 3.9 on p. 37

  6. The kernel is negative definite A Negative Definite Kernel • Does not satisfy Mercer’s conditions • Oliv L. Mangansarian used this kernel to solve XOR classification problem • Generalized Support Vector Machine

  7. Key assumption: Training and testing data are generated i.i.d. ( i.e. “average Probably Approximately Correct Learning pac Model fixed but unknown distribution according to an • When we evaluate the “quality” of a hypothesis (classification function) we should take the into account unknown distribution error” or “expected error” ) made by the • We call such measure risk functional and denote it as

  8. Let training be a set of examples choseni.i.d.according to as ar.v. • Treat the generalization error depending on the random selection of r.v. • Find a bound of the trail of the distribution of in the form ,where and is a function of is the confidence level of the error bound which is given by learner Generalization Error of pac Model

  9. will be less • The error made by the hypothesis then the error bound that is not depend on the unknown distribution Probably Approximately Correct • We assert: or

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