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Neural Processing Letters

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01-04-2014

Learning to Rank with Extreme Learning Machine

Authors: Weiwei Zong, Guang-Bin Huang

Published in: Neural Processing Letters | Issue 2/2014

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Abstract

Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on. From machine learning point of view, extreme learning machine actually provides a unified framework where the aforementioned algorithms can be considered as special cases. In this paper, pointwise ELM and pairwise ELM are proposed to learn relevance ranking problems for the first time. In particular, ELM type of linear random node is newly proposed together with kernel version of ELM to be linear as well. The famous publicly available dataset collection LETOR is tested to compare ELM-based ranking algorithms with state-of-art linear ranking algorithms.

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Title: Learning to Rank with Extreme Learning Machine
Authors: Weiwei Zong
Guang-Bin Huang
Publication date: 01-04-2014
Publisher: Springer US
Published in: Neural Processing Letters / Issue 2/2014
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-013-9295-8

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New Results on Exponential Convergence for HRNNs with Continuously Distributed Delays in the Leakage Terms

Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting

Approximation and Estimation Bounds for Subsets of Reproducing Kernel Kreǐn Spaces