Robust regression and outlier detection
Robust regression and outlier detection
Consistency of multilayer perceptron regression estimators
Neural Networks
Machine Learning
Generalized projection pursuit regression
SIAM Journal on Scientific Computing
Modeling for Optimal Probability Prediction
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Efficient search with changing similarity measures on large multimedia datasets
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
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The problem of regression is to estimate the value of a dependent numeric variable based on the values of one or more independent variables. Regression algorithms are used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Although this problem has been studied extensively, most of these approaches are not generic in that they require the user to make an intelligent guess about the form of the regression equation. In this paper we present a new regression algorithm PAGER - Parameterless, Accurate, Generic, Efficient kNN-based Regression. PAGER is also simple and outlier-resilient. These desirable features make PAGER a very attractive alternative to existing approaches. Our experimental study compares PAGER with 12 other algorithms on 4 standard real datasets, and shows that PAGER is more accurate than its competitors.