TY - GEN
T1 - Learning to Rank: A Progressive Neural Network Learning Approach
AU - Tran, Dat Thanh
AU - Iosifidis, Alexandros
N1 - EXT="Iosifidis, Alexandros"
PY - 2019/4/17
Y1 - 2019/4/17
N2 - 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.
AB - 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.
KW - feedforward neural nets
KW - image retrieval
KW - learning (artificial intelligence)
KW - learning to rank
KW - random forest
KW - storage requirement
KW - Web search ranking
KW - progressive neural network learning approach
KW - LambdaMART
KW - image retrieval tasks
KW - ranking problem
KW - Generalized Operational Perceptrons
KW - single heterogeneous feedforward network architecture
KW - significant operating cost
KW - ensemble model
KW - computation requirement
KW - state-of-the-art ranking systems
KW - information retrieval system
KW - essential component
KW - Generalized Operational Perceptron
KW - Progressive Neural Network Learning
U2 - 10.1109/ICASSP.2019.8683711
DO - 10.1109/ICASSP.2019.8683711
M3 - Conference contribution
SN - 978-1-4799-8132-8
T3 - IEEE International Conference on Acoustics, Speech and Signal Processing
SP - 8355
EP - 8359
BT - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PB - IEEE
T2 - IEEE International Conference on Acoustics, Speech and Signal Processing
Y2 - 1 January 1900 through 1 January 2000
ER -