Theoretical Analyses on Ensemble and Multiple Kernel Regressors
نویسندگان
چکیده
For the last few decades, learning based on multiple kernels, such as the ensemble kernel regressor and the multiple kernel regressor, has attracted much attention in the field of machine learning. Although its efficacy was revealed numerically in many works, its theoretical ground is not investigated sufficiently. In this paper, we discuss regression problems with a class of kernels whose corresponding reproducing kernel Hilbert spaces have a common subspace with an invariant metric and show that the ensemble kernel regressor (the mean of kernel regressors with those kernels) gives a better learning result than the multiple kernel regressor (the kernel regressor with the sum of those kernels) in terms of the generalization ability of a model space.
منابع مشابه
Ensemble and Multiple Kernel Regressors: Which Is Better?
Title Ensemble and Multiple Kernel Regressors : Which Is Better? Author(s) Tanaka, Akira; Takebayashi, Hirofumi; Takigawa, Ichigaku; Imai, Hideyuki; Kudo, Mineichi Citation IEICE transactions on fundamentals of electronics communications and computer sciences, E98A(11): 2315-2324 Issue Date 2015-11 Doc URL http://hdl.handle.net/2115/60358 Right copyright©2015 IEICE Type article File Information...
متن کاملKernel-based Inference in Time-varying Coefficient Cointegrating Regression
This paper studies nonlinear cointegrating models with time-varying coefficients and multiple nonstationary regressors using classic kernel smoothing methods to estimate the coefficient functions. Extending earlier work on nonstationary kernel regression to take account of practical features of the data, we allow the regressors to be cointegrated and to embody a mixture of stochastic and determ...
متن کاملBoosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting
Accurate electricity load forecasting is of crucial importance for power system operation and smart grid energy management. Different factors, such as weather conditions, lagged values, and day types may affect electricity load consumption. We propose to use multiple kernel learning (MKL) for electricity load forecasting, as it provides more flexibilities than traditional kernel methods. Comput...
متن کاملA Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors
We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal compon...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014