Sparse Bayesian Modeling With Adaptive Kernel Learning
نویسندگان
چکیده
منابع مشابه
Sparse kernel learning with LASSO and Bayesian inference algorithm
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In Internationa...
متن کاملVolatility Forecasting with Sparse Bayesian Kernel Models
Motivated by previous findings that discretization of financial time series can effectively filter the data and reduce the noise, this experimental study, performed in a realistic setting of trading straddles via predicting volatility, compares trading performances of symbol-based models with those of probabilistic models operating on real-valued sequences. We show that carefully designed proba...
متن کاملAdaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Kernel based machine learning techniques have been widely used to tackle problems of function approximation and regression estimation. Relevance vector machine (RVM) has state of the art performance in sparse regression. As a popular and competent kernel function in machine learning, conventional Gaussian kernel has unified kernel width with each of basis functions, which make impliedly a basic...
متن کاملSparse Bayesian kernel logistic regression
In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based MacKay’s evidence approximation. The model is re-parameterised such that an isotropic Gaussian prior over parameters in the kernel induced feature space is replaced by an isotropic Gaussian prior over the transformed parameters, facilitating a Bayesian analysis using stan...
متن کاملKernel Regression with Sparse Metric Learning
Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed by a distancebased kernel function and they strongly depend on the distances b...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks
سال: 2009
ISSN: 1045-9227,1941-0093
DOI: 10.1109/tnn.2009.2014060