Least Squares Support Vector Machine Classifiers
Neural Processing Letters
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Letters: Convex incremental extreme learning machine
Neurocomputing
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Statistical Analysis of Bayes Optimal Subset Ranking
IEEE Transactions on Information Theory
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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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.