International Journal of Computer Vision
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A maximal figure-of-merit learning approach to text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Text classification with kernels on the multinomial manifold
Proceedings of the 28th 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
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An ensemble classifier learning approach to ROC optimization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Early exit optimizations for additive machine learned ranking systems
Proceedings of the third ACM international conference on Web search and data mining
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Improving Semantic Concept Detection Through Optimizing Ranking Function
IEEE Transactions on Multimedia
Explicit performance metric optimization for fusion-based video retrieval
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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We propose an efficient algorithm that directly optimizes a ranking performance measure, with a focus on class average precision (AP). Instead of using pair-wise ranking approximation in defining a loss function by conventional approaches, we use an efficient gradient-based approach that approximates a discrete ranking performance measure. In particular, AP is considered as a staircase function with respect to each individual sample score after rank ordering is applied to all samples. Then, a combination of sigmoid functions is applied to approximate the staircase AP function as a continuous and differntiable function of the model parameters used to compute the sample scores. Compared to the use of pair-wise rankings, the proposed approach substantially reduces the computational complexity to a manageable level when estimating model parameters with a gradient descent algorithm. In terms of explicitly optimizing a target performance metric, the proposed algorithm can be considered as an extension of maximal figure-of-merit (MFoM) learning to optimization of a ranking performance measure. Our experiments on two challenging image-retrieval datasets showcased the usefulness of the proposed framework in both improving AP and achieving learning efficiency.