Add to ORKGIn this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages
Vukanovicz S, Schulz A, Haschke R, Ritter H. Learning the Appropriate Model Population Structures fo...
Learning in real-time applications, e.g., online approximation of the inverse dy-namics model for mo...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
Locally weighted projection regression (LWPR) is a new algorithm for incremental non-linear function...
Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear func- ti...
Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function ...
Locally weighted projection regression is a new algorithm that achieves nonlinear function approxima...
Abstract. This paper surveys locally weighted learning, a form of lazy learning and memory-based lea...
Locally weighted regression is a non-parametric technique of regression that is capable of coping wi...
Abstract: We introduce a new algorithm, Locally Weighted Projection Regression (LWPR), for increment...
We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a ra...
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. ...
In this paper we introduce an algorithm for approximatinga function by means of local models. We ass...
Locally-weighted regression is a computationally-efficient technique for non-linear regression. How...
Abstract We propose a novel linear dimensionality reduction algorithm, namely Locally Regressive Pro...
Vukanovicz S, Schulz A, Haschke R, Ritter H. Learning the Appropriate Model Population Structures fo...
Learning in real-time applications, e.g., online approximation of the inverse dy-namics model for mo...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
Locally weighted projection regression (LWPR) is a new algorithm for incremental non-linear function...
Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear func- ti...
Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function ...
Locally weighted projection regression is a new algorithm that achieves nonlinear function approxima...
Abstract. This paper surveys locally weighted learning, a form of lazy learning and memory-based lea...
Locally weighted regression is a non-parametric technique of regression that is capable of coping wi...
Abstract: We introduce a new algorithm, Locally Weighted Projection Regression (LWPR), for increment...
We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a ra...
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. ...
In this paper we introduce an algorithm for approximatinga function by means of local models. We ass...
Locally-weighted regression is a computationally-efficient technique for non-linear regression. How...
Abstract We propose a novel linear dimensionality reduction algorithm, namely Locally Regressive Pro...
Vukanovicz S, Schulz A, Haschke R, Ritter H. Learning the Appropriate Model Population Structures fo...
Learning in real-time applications, e.g., online approximation of the inverse dy-namics model for mo...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...