Corpus ID: 7138996

Ensembles of Kernel Predictors

  title={Ensembles of Kernel Predictors},
  author={Corinna Cortes and M. Mohri and Afshin Rostamizadeh},
  • Corinna Cortes, M. Mohri, Afshin Rostamizadeh
  • Published in UAI 2011
  • Computer Science, Mathematics
  • This paper examines the problem of learning with a finite and possibly large set of p base kernels. It presents a theoretical and empirical analysis of an approach addressing this problem based on ensembles of kernel predictors. This includes novel theoretical guarantees based on the Rademacher complexity of the corresponding hypothesis sets, the introduction and analysis of a learning algorithm based on these hypothesis sets, and a series of experiments using ensembles of kernel predictors… CONTINUE READING
    13 Citations

    Figures, Tables, and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Voted Kernel Regularization
    • PDF
    Kernel Extraction via Voted Risk Minimization
    • 3
    • PDF
    Algorithms for Learning Kernels Based on Centered Alignment
    • 288
    • PDF
    Generalized Multiple Kernel Learning With Data-Dependent Priors
    • 12
    Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
    • 7
    • PDF
    Learning Triggering Kernels for Multi-dimensional Hawkes Processes
    • 159
    • PDF
    Structured prediction for feature selection and performance evaluation
    • Highly Influenced
    • PDF


    Generalization Bounds for Learning Kernels
    • 113
    • PDF
    Learning the Kernel with Hyperkernels
    • 347
    • PDF
    Learning Convex Combinations of Continuously Parameterized Basic Kernels
    • 107
    • PDF
    Generalization Bounds for Learning the Kernel Problem
    • 18
    • PDF
    Two-Stage Learning Kernel Algorithms
    • 181
    • PDF
    A DC-programming algorithm for kernel selection
    • 114
    • PDF
    Learning the Kernel Function via Regularization
    • 401
    • PDF
    Sparse Recovery in Large Ensembles of Kernel Machines On-Line Learning and Bandits
    • 72
    • PDF
    Nonstationary kernel combination
    • 89
    • PDF
    Learning Bounds for Support Vector Machines with Learned Kernels
    • 90
    • PDF