Add to ORKGLazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered relevant according to a distance measure. In this paper we propose a datadriven method to select on a query-by-query basis the optimal number of neighbors to be considered for each prediction. As an efficient way to identify and validate local models, the recursive least squares algorithm is introduced in the context of local approximation and lazy learning. Furthermore, beside the winner-takes-all strategy for model selection, a local combination of the most promising models is explored. The method proposed is tested on six different datasets and compared with a sta...
Abstract. This paper presents local methods for modeling and control of discrete-time unknown nonlin...
Lazy learning methods have been used to deal with problems in which the learning examples are not ev...
In the domain of inductive learning from examples, usually, training data are not evenly distributed...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Lazy Learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Local learning techniques, for each query, extract a predic-tion interpolating locally the neighbori...
Linear and nonlinear regression problems are very common in different fields of science and engineer...
Lazy local learning methods train a classifier “on the fly ” at test time, using only a subset of th...
In this paper, we identify a similar linear model in a local way, using a lazy learning algorithm. T...
In this paper, we propose a lazy learning strategy for building classification learning models. Inst...
An approach is presented to learning high dimensional functions in the case where the learning algor...
Local learning employs locality adjusting mechanisms to give local function estimation for each quer...
The traditional approach to supervised learning is global modeling which describes the relationship ...
Abstract. This paper presents local methods for modeling and control of discrete-time unknown nonlin...
Lazy learning methods have been used to deal with problems in which the learning examples are not ev...
In the domain of inductive learning from examples, usually, training data are not evenly distributed...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Lazy Learning is a memory-based technique that, once a query is received, extracts a prediction inte...
Local learning techniques, for each query, extract a predic-tion interpolating locally the neighbori...
Linear and nonlinear regression problems are very common in different fields of science and engineer...
Lazy local learning methods train a classifier “on the fly ” at test time, using only a subset of th...
In this paper, we identify a similar linear model in a local way, using a lazy learning algorithm. T...
In this paper, we propose a lazy learning strategy for building classification learning models. Inst...
An approach is presented to learning high dimensional functions in the case where the learning algor...
Local learning employs locality adjusting mechanisms to give local function estimation for each quer...
The traditional approach to supervised learning is global modeling which describes the relationship ...
Abstract. This paper presents local methods for modeling and control of discrete-time unknown nonlin...
Lazy learning methods have been used to deal with problems in which the learning examples are not ev...
In the domain of inductive learning from examples, usually, training data are not evenly distributed...