Neural networks: a systematic introduction
Neural networks: a systematic introduction
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
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
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Learning to rank at query-time using association rules
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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While conventional ranking algorithms, such as the PageRank, rely on the web structure to decide the relevancy of a web page, learning to rank seeks a function capable of ordering a set of instances using a supervised learning approach. Learning to rank has gained increasing popularity in information retrieval and machine learning communities. In this paper, we propose a novel nonlinear perceptron method for rank learning. The proposed method is an online algorithm and simple to implement. It introduces a kernel function to map the original feature space into a nonlinear space and employs a perceptron method to minimize the ranking error by avoiding converging to a solution near the decision boundary and alleviating the effect of outliers in the training dataset. Furthermore, unlike existing approaches such as RankSVM and RankBoost, the proposed method is scalable to large datasets for online learning. Experimental results on benchmark corpora show that our approach is more efficient and achieves higher or comparable accuracies in instance ranking than state of the art methods such as FRank, RankSVM and RankBoost.