An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Recursive Lazy Learning for Modeling and Control
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Letters: Convex incremental extreme learning machine
Neurocomputing
Letters: Fully complex extreme learning machine
Neurocomputing
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Multiresponse sparse regression with application to multidimensional scaling
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Mutual information and k-nearest neighbors approximator for time series prediction
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed.