Add to ORKGA unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach
Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimati...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A unified approach is proposed for sparse kernel data modelling that includes regression and classif...
A unified approach is proposed for sparse kernel data modelling that includes regression and classif...
A unified approach is proposed for data modelling that includes supervised regression and classifica...
The objective of modelling from data is not that the model simply fits the training data well. Rathe...
Abstract—This paper presents an efficient construction algo-rithm for obtaining sparse kernel densit...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
An automatic algorithm is derived for constructing kernel density estimates based on a regression ap...
The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates...
Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing lead...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A generalized or tunable-kernel model is proposed for probability density function estimation based ...
The paper proposes to combine an orthogonal least squares (OLS) model selection with local regularis...
Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimati...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
A unified approach is proposed for sparse kernel data modelling that includes regression and classif...
A unified approach is proposed for sparse kernel data modelling that includes regression and classif...
A unified approach is proposed for data modelling that includes supervised regression and classifica...
The objective of modelling from data is not that the model simply fits the training data well. Rathe...
Abstract—This paper presents an efficient construction algo-rithm for obtaining sparse kernel densit...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...
An automatic algorithm is derived for constructing kernel density estimates based on a regression ap...
The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates...
Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing lead...
A novel technique is proposed to construct sparse regression models based on the orthogonal least sq...
A generalized or tunable-kernel model is proposed for probability density function estimation based ...
The paper proposes to combine an orthogonal least squares (OLS) model selection with local regularis...
Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimati...
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out...
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights ...