Learning with Kernels

Schölkopf, & Smola. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.

Type

Book

Intellectual base

Review or monograph

Year

2002

ISBN

9780262194754

Link

https://openlibrary.org/books/OL18171178Mmatched

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Domain-specific

• Bayesian Interpretation of Kernel Regularization
  • Support-vector machines use hinge or epsilon-insensitive losses and have different estimands

    This sourceThe book establishes the loss half of this sentence - the soft-margin (hinge) loss for SV classification and the epsilon-insensitive loss for SV regression, both as regularized risk minimization in an RKHS (chs. 3, 7 and 9); the claim that these losses target different estimands is a further step it does not take.

    Supported in partVerified against the source

    Read by an automated reader; not a human review. How support was checked

• Positive-definite kernel
  • … x-y\rVert^2)\) for \(\gamma>0\) is positive definite on Euclidean space and corresponds to an inner-product feature representation. to A covariance model uses a positive-definite kernel so every finite vector of modeled observations has a positive-semidefinite covariance matrix. demonstrates that continuity.

Verification

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