SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fast Nearest Neighbor Search in High-Dimensional Space
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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 method of learning weighted similarity function to improve the performance of nearest neighbor
Information Sciences: an International Journal
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IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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IEEE Transactions on Information Theory
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Regression is the study of functional dependency of one numeric variable with respect to another. In this paper, we present a novel, efficient, binary search based regression algorithm having the advantage of low computational complexity. These desirable features make BINER a very attractive alternative to existing approaches. The algorithm is interesting because instead of directly predicting the value of response variable, it recursively narrows down the range in which the response variable lies. Our empirical experiments with several real world datasets show that our algorithm, outperforms current state of art approaches and is faster by an order of magnitude.