Download
1 / 9
90 likes | 173 Views
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.
E N D
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:
Characterization of Kernels Be Positive Semi-definite Assume: Let where
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.
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:
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
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
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
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
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
