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Learning to Rank: A Progressive Neural Network Learning Approach

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Details

Original languageEnglish
Title of host publicationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages8355-8359
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
ISBN (Print)978-1-4799-8132-8
DOIs
Publication statusPublished - 17 Apr 2019
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech and Signal Processing -
Duration: 1 Jan 19001 Jan 2000

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Period1/01/001/01/00

Abstract

Learning to rank is an essential component in an information retrieval system. The state-of-the-art ranking systems are often based on an ensemble of classifiers, such as Random Forest or LambdaMART, which aggregates the ranking outputs produced by thousands of classifiers. The storage and computation requirement of an ensemble model is usually very high, imposing a significant operating cost to the retrieval system. To tackle this problem, we propose an algorithm that adaptively learns a single heterogeneous feedforward network architecture, composing of Generalized Operational Perceptrons, given a ranking problem. Experimental results in web search ranking and image retrieval tasks show that the proposed algorithm compares favourably to the related algorithms.

Keywords

  • feedforward neural nets, image retrieval, learning (artificial intelligence), learning to rank, random forest, storage requirement, Web search ranking, progressive neural network learning approach, LambdaMART, image retrieval tasks, ranking problem, Generalized Operational Perceptrons, single heterogeneous feedforward network architecture, significant operating cost, ensemble model, computation requirement, state-of-the-art ranking systems, information retrieval system, essential component, Generalized Operational Perceptron, Progressive Neural Network Learning

Publication forum classification

Field of science, Statistics Finland